16x16.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f20ba5d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from skimage.io import imread\n",
    "import cv2\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ed0580a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Cyst'\n",
    "os.makedirs('resized dataset/Resized Folder 16x16/Cyst', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (16, 16))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 16x16/Cyst/Cyst- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6f68811c",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Normal'\n",
    "os.makedirs('resized dataset/Resized Folder 16x16/Normal', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (16, 16))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 16x16/Normal/Normal- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "108cb998",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Stone'\n",
    "os.makedirs('resized dataset/Resized Folder 16x16/Stone', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (16, 16))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 16x16/Stone/Stone- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "16dab6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Tumor'\n",
    "os.makedirs('resized dataset/Resized Folder 16x16/Tumor', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (16, 16))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 16x16/Tumor/Tumor- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d2ff0ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path('resized dataset/Resized Folder 16x16')\n",
    "train_dir = data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6b0f8794",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases_dir = train_dir / 'Normal'\n",
    "Cyst_Cases_dir = train_dir / 'Cyst'\n",
    "Stone_Cases_dir = train_dir / 'Stone'\n",
    "Tumor_Cases_dir = train_dir / 'Tumor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "013a9442",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases = Normal_Cases_dir.glob('*.jpg') \n",
    "Cyst_Cases = Cyst_Cases_dir.glob('*.jpg') \n",
    "Stone_Cases = Stone_Cases_dir.glob('*.jpg')\n",
    "Tumor_Cases = Tumor_Cases_dir.glob('*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "86351d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fb37ced5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Cyst_Cases:\n",
    "    train_data.append((img, 0))\n",
    "\n",
    "for img in Normal_Cases:\n",
    "    train_data.append((img, 1))\n",
    "\n",
    "for img in Stone_Cases:\n",
    "    train_data.append((img, 2))\n",
    "\n",
    "for img in Tumor_Cases:\n",
    "    train_data.append((img, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "db428cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  </tbody>\n",
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       "<p>12446 rows × 2 columns</p>\n",
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      "text/plain": [
       "                                                   image  label\n",
       "0      resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "1      resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "2      resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "3      resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "4      resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "...                                                  ...    ...\n",
       "12441  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "12442  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "12443  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "12444  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "12445  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "\n",
       "[12446 rows x 2 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.DataFrame(train_data, columns=['image', 'label'], index=None)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "1deb6b86",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "                                               image  label\n",
       "0  resized dataset/Resized Folder 16x16/Tumor/Tum...      3\n",
       "1  resized dataset/Resized Folder 16x16/Stone/Sto...      2\n",
       "2  resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "3  resized dataset/Resized Folder 16x16/Cyst/Cyst...      0\n",
       "4  resized dataset/Resized Folder 16x16/Cyst/Cyst...      0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data = train_data.sample(frac=1.).reset_index(drop=True) \n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1cce76be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 0, 1])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "445572e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12446, 2)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "69bd6789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    5077\n",
       "0    3709\n",
       "3    2283\n",
       "2    1377\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_count = train_data['label'].value_counts()\n",
    "cases_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "724828f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m4/0yq10js97k90dzpb9_y3tfzc0000gn/T/ipykernel_27774/707280772.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x = cases_count.index, y = cases_count.values, palette='bright', edgecolor='black')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "sns.barplot(x = cases_count.index, y = cases_count.values, palette='bright', edgecolor='black')\n",
    "plt.title('Number of Cases', fontsize=14)\n",
    "plt.xlabel('Case Type', fontsize = 12)\n",
    "plt.ylabel('Count', fontsize = 12)\n",
    "plt.xticks(range(len(cases_count.index)),['Cyst(0)', 'Normal(1)','Stone(2)','Tumor(3)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cacdf9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cyst_Samples = (train_data[train_data['label'] == 0]['image'].iloc[:5]).tolist()\n",
    "Normal_Samples = (train_data[train_data['label'] == 1]['image'].iloc[:5]).tolist()\n",
    "Stone_Samples = (train_data[train_data['label'] == 2]['image'].iloc[:5]).tolist()\n",
    "Tumor_Samples = (train_data[train_data['label'] == 3]['image'].iloc[:5]).tolist()\n",
    "\n",
    "\n",
    "samples = Cyst_Samples + Normal_Samples + Stone_Samples + Tumor_Samples\n",
    "\n",
    "del Cyst_Samples, Normal_Samples, Stone_Samples, Tumor_Samples\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "129898bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAIAoIyUAAAAAAAAAACDKSAkAAAAAAAAAAIgyUgIAAAAAAAAAAKKMlAAAAAAAAAAAgKjeZDKZXOsXAQAAAAAAAAAAdJe/pAQAAAAAAAAAAEQZKQEAAAAAAAAAAFFGSgAAAAAAAAAAQJSREgAAAAAAAAAAEGWkBAAAAAAAAAAARBkpAQAAAAAAAAAAUUZKAAAAAAAAAABAlJESAAAAAAAAAAAQZaQEAAAAAAAAAABE/QtmND7wt36ojAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 3000x3000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(4, 5,figsize=(30,30)) \n",
    "\n",
    "for i in range(20):\n",
    "    img = imread(samples[i]) \n",
    "    ax[i//5, i%5].imshow(img, cmap='gray') \n",
    "    \n",
    "\n",
    "    if i<5:\n",
    "        ax[i//5, i%5].set_title(\"Cyst_samples\")\n",
    "    elif i<10:\n",
    "        ax[i//5, i%5].set_title(\"Normal_samples\")\n",
    "    elif i<15:\n",
    "        ax[i//5, i%5].set_title(\"Stone_samples\")\n",
    "    elif i<20:\n",
    "        ax[i//5, i%5].set_title(\"Tumor_samples\")\n",
    "    \n",
    " \n",
    "    ax[i//5, i%5].axis('off')\n",
    "    ax[i//5, i%5].set_aspect('auto')\n",
    "\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "019be1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Normal_Cases_dir = train_dir / 'Normal'\n",
    "Cyst_Cases_dir = train_dir / 'Cyst'\n",
    "Stone_Cases_dir = train_dir / 'Stone'\n",
    "Tumor_Cases_dir = train_dir / 'Tumor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f9fe6028",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases = Normal_Cases_dir.glob('*.jpg')\n",
    "Cyst_Cases = Cyst_Cases_dir.glob('*.jpg')\n",
    "Stone_Cases = Stone_Cases_dir.glob('*.jpg')\n",
    "Tumor_Cases = Tumor_Cases_dir.glob('*.jpg')\n",
    "train_data = []\n",
    "train_labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7e068ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for img in Cyst_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1: \n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) \n",
    "    img = np.array(img)\n",
    "    img = img/255  \n",
    "    label = 'Cyst' \n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c7b1d7a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for img in Normal_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = 'Normal'\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e61237be",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Stone_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img,img,img])\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = \"Stone\"\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "100dd1dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Tumor_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = \"Tumor\"\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4d25dfc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of validation example:  (12446, 16, 16, 3)\n",
      "Total number of Labels :  (12446,)\n"
     ]
    }
   ],
   "source": [
    "train_data1 = np.array(train_data) \n",
    "train_labels1 = np.array(train_labels)\n",
    "print('Total number of validation example: ', train_data1.shape)\n",
    "print('Total number of Labels : ', train_labels1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dac3401",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e6954e54",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12442</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12443</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12444</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12445</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12446 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label\n",
       "0       Cyst\n",
       "1       Cyst\n",
       "2       Cyst\n",
       "3       Cyst\n",
       "4       Cyst\n",
       "...      ...\n",
       "12441  Tumor\n",
       "12442  Tumor\n",
       "12443  Tumor\n",
       "12444  Tumor\n",
       "12445  Tumor\n",
       "\n",
       "[12446 rows x 1 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1 = pd.DataFrame(train_labels1, columns = ['label'],index=None)\n",
    "train_labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "05c72a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cyst', 'Normal', 'Stone', 'Tumor'], dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7d2b8674",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels1['label'] = train_labels1['label'].map({'Cyst':0,'Normal':1,'Stone':2,'Tumor':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0cb5492e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12442</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12443</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12444</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12445</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12446 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label\n",
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "...      ...\n",
       "12441      3\n",
       "12442      3\n",
       "12443      3\n",
       "12444      3\n",
       "12445      3\n",
       "\n",
       "[12446 rows x 1 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "aec9aefe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12446, 16, 16, 3)\n",
      "(12446, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_data1.shape)\n",
    "print(train_labels1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c7da7867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d70badda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original subset data shape: (1244, 768)\n",
      "Resampled subset data shape: (2092, 16, 16, 3)\n",
      "Resampled subset labels shape: (2092, 1)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming train_data1 is your original dataset\n",
    "# Assuming train_labels1 is your corresponding labels\n",
    "\n",
    "# Create a subset of the data\n",
    "subset_fraction = 0.1  # 10% of the data\n",
    "subset_data, _, subset_labels, _ = train_test_split(\n",
    "    train_data1, train_labels1, test_size=(1 - subset_fraction), random_state=42\n",
    ")\n",
    "\n",
    "# Reshape the subset data if needed\n",
    "subset_rows = len(subset_data)\n",
    "subset_data = subset_data.reshape(subset_rows, -1)\n",
    "\n",
    "# Apply SMOTE to the subset\n",
    "smote = SMOTE()\n",
    "subset_data_resampled, subset_labels_resampled = smote.fit_resample(subset_data, subset_labels)\n",
    "\n",
    "# Reshape the resampled data back to the original shape if needed\n",
    "subset_data_resampled = subset_data_resampled.reshape(subset_data_resampled.shape[0], 16, 16, 3)\n",
    "\n",
    "# Print the shapes to check\n",
    "print(\"Original subset data shape:\", subset_data.shape)\n",
    "print(\"Resampled subset data shape:\", subset_data_resampled.shape)\n",
    "print(\"Resampled subset labels shape:\", subset_labels_resampled.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "bb0f5ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m4/0yq10js97k90dzpb9_y3tfzc0000gn/T/ipykernel_27774/1410693467.py:4: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=cases_count1.index, y=cases_count1.values, palette='bright', edgecolor='black')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cases_count1 = subset_labels_resampled['label'].value_counts() \n",
    "\n",
    "plt.figure(figsize=(8,6)) \n",
    "sns.barplot(x=cases_count1.index, y=cases_count1.values, palette='bright', edgecolor='black')\n",
    "plt.title('Number of cases', fontsize = 14)\n",
    "plt.xlabel('Case Type',fontsize = 12)\n",
    "plt.ylabel('Count', fontsize = 12)\n",
    "plt.xticks(range(len(cases_count1.index)), ['Cyst(0)', 'Normal(1)', 'Stone(2)', 'Tumor(3)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "50ca916b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 16, 16, 3)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_data_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "077fb245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 16, 16, 3)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data2 = subset_data_resampled.reshape(-1,16,16,3) # Converting 2D array to 4D Array\n",
    "train_data2.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "676fa9f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "93137916",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "subset_size = 1000  # Adjust the size based on your available memory\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data2[:subset_size], subset_labels_resampled[:subset_size], test_size=0.20, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "be2dbb3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data X :  (720, 16, 16, 3)\n",
      "Testing Data X :  (200, 16, 16, 3)\n",
      "Training Data y :  (720, 1)\n",
      "Testing Data y :  (200, 1)\n",
      "Validating Data X :  (80, 16, 16, 3)\n",
      "Validating Data y (80, 1)\n",
      "Image Size :  (16, 16, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Data X : \",X_train.shape)\n",
    "print(\"Testing Data X : \",X_test.shape)\n",
    "print(\"Training Data y : \",y_train.shape)\n",
    "print(\"Testing Data y : \",y_test.shape)\n",
    "print(\"Validating Data X : \",X_val.shape)\n",
    "print(\"Validating Data y\",y_val.shape)\n",
    "print(\"Image Size : \",X_train[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5a11474f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "data_augmentation = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "                    rotation_range=30, \n",
    "                    horizontal_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "97e657c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_4 (Conv2D)           (None, 14, 14, 64)        1792      \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 7, 7, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 5, 5, 64)          36928     \n",
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " max_pooling2d_5 (MaxPoolin  (None, 2, 2, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                8224      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 47076 (183.89 KB)\n",
      "Trainable params: 47076 (183.89 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "\n",
    "model = Sequential([\n",
    "    Conv2D(64, (3, 3), activation='relu', input_shape=(16, 16, 3)),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(4, activation='softmax')\n",
    "])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3ea74248",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 0s 6ms/step - loss: 1.2206 - accuracy: 0.4486 - val_loss: 1.0624 - val_accuracy: 0.6000\n",
      "Epoch 2/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 1.0260 - accuracy: 0.6181 - val_loss: 0.9215 - val_accuracy: 0.6125\n",
      "Epoch 3/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.9553 - accuracy: 0.6306 - val_loss: 0.8714 - val_accuracy: 0.7000\n",
      "Epoch 4/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.8813 - accuracy: 0.6556 - val_loss: 0.9056 - val_accuracy: 0.6500\n",
      "Epoch 5/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.8120 - accuracy: 0.6889 - val_loss: 0.7929 - val_accuracy: 0.7375\n",
      "Epoch 6/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.7445 - accuracy: 0.7278 - val_loss: 0.7603 - val_accuracy: 0.7125\n",
      "Epoch 7/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.7224 - accuracy: 0.7153 - val_loss: 0.7406 - val_accuracy: 0.7375\n",
      "Epoch 8/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.6311 - accuracy: 0.7708 - val_loss: 0.7390 - val_accuracy: 0.7250\n",
      "Epoch 9/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5650 - accuracy: 0.8069 - val_loss: 0.6919 - val_accuracy: 0.7625\n",
      "Epoch 10/10\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5347 - accuracy: 0.8292 - val_loss: 0.6222 - val_accuracy: 0.7500\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(np.array(X_train), np.array(y_train), epochs=10, validation_data=(np.array(X_val), np.array(y_val)))\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Training Loss Curve\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Training accuracy Curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "6a7caf4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2d568a8e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "7/7 [==============================] - 0s 1ms/step\n",
      "[[5.75317860e-01 7.23391771e-02 1.56131342e-01 1.96211606e-01]\n",
      " [3.93957794e-01 7.09626898e-02 1.18915796e-01 4.16163713e-01]\n",
      " [1.01462090e-02 7.04991758e-01 1.20674789e-01 1.64187327e-01]\n",
      " [5.48678279e-01 1.49766997e-01 1.35960877e-01 1.65593877e-01]\n",
      " [8.68786156e-01 1.16426963e-02 6.71478137e-02 5.24233356e-02]\n",
      " [9.83876176e-04 5.96053541e-01 4.15527187e-02 3.61409843e-01]\n",
      " [2.31903289e-02 5.12766659e-01 1.12205006e-01 3.51837963e-01]\n",
      " [5.00698209e-01 3.22348401e-02 1.16618641e-01 3.50448340e-01]\n",
      " [6.67510927e-01 8.10762271e-02 1.62115589e-01 8.92971978e-02]\n",
      " [1.20672798e-02 8.30183923e-02 6.25366122e-02 8.42377722e-01]\n",
      " [2.13623769e-03 6.14788651e-01 1.65387187e-02 3.66536409e-01]\n",
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      " [2.03510951e-02 7.23545849e-02 5.43988533e-02 8.52895439e-01]\n",
      " [5.90146985e-04 8.81751299e-01 3.72171141e-02 8.04414824e-02]\n",
      " [2.76567880e-03 4.48882908e-01 9.58896056e-02 4.52461839e-01]\n",
      " [6.15344688e-05 4.12711799e-01 1.86107755e-02 5.68615854e-01]\n",
      " [1.55347947e-03 7.98221946e-01 5.58125488e-02 1.44411951e-01]\n",
      " [1.20164805e-04 8.51250648e-01 2.60983221e-02 1.22530818e-01]\n",
      " [5.07591784e-01 1.31272838e-01 2.11979419e-01 1.49155915e-01]\n",
      " [8.68529081e-01 1.81217920e-02 6.43884838e-02 4.89606857e-02]\n",
      " [5.84688544e-01 4.13889810e-02 1.59501821e-01 2.14420706e-01]\n",
      " [9.76501763e-01 4.84496966e-04 1.24998055e-02 1.05139520e-02]\n",
      " [5.44388473e-01 9.75082535e-03 2.62050301e-01 1.83810338e-01]\n",
      " [1.74942032e-01 4.32589561e-01 2.66173869e-01 1.26294538e-01]\n",
      " [4.50672880e-02 9.18606818e-02 9.23073664e-02 7.70764649e-01]\n",
      " [9.33501360e-06 8.68157566e-01 2.82614422e-03 1.29006967e-01]\n",
      " [8.81647021e-02 1.09155200e-01 1.16695262e-01 6.85984850e-01]\n",
      " [7.50594493e-03 7.76366532e-01 2.82991957e-02 1.87828392e-01]\n",
      " [8.20508957e-01 1.50774103e-02 7.84695148e-02 8.59441459e-02]\n",
      " [2.68893450e-01 1.20110847e-01 1.20179683e-01 4.90815997e-01]\n",
      " [3.87472741e-04 7.23717391e-01 2.73185782e-02 2.48576656e-01]\n",
      " [3.51764937e-03 8.06256831e-01 7.29202777e-02 1.17305294e-01]\n",
      " [4.60260689e-01 3.93829681e-02 3.32415551e-01 1.67940810e-01]\n",
      " [7.12082267e-01 1.83133520e-02 1.14191577e-01 1.55412823e-01]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "a = model.predict(X_test)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "efcfb265",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 3, 1, 0, 0, 1, 1, 0, 0, 3, 1, 0, 1, 0, 1, 0, 1, 1, 3, 0, 3, 0, 3, 3, 3, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 3, 0, 1, 3, 1, 0, 0, 1, 1, 0, 1, 1, 3, 1, 3, 1, 1, 0, 3, 3, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 3, 0, 0, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 0, 2, 1, 0, 3, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 3, 1, 3, 1, 0, 0, 3, 3, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 3, 1, 3, 3, 0, 1, 3, 3, 1, 0, 1, 3, 1, 0, 3, 3, 3, 1, 1, 3, 0, 1, 1, 3, 1, 3, 1, 3, 1, 3, 3, 1, 1, 0, 0, 0, 0, 0, 1, 3, 1, 3, 1, 0, 3, 1, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "b = []\n",
    "\n",
    "for i in a:\n",
    "    if i[0] > i[1] and i[0] > i[2] and i[0] > i[3]:\n",
    "        b.append(0)\n",
    "    elif i[1] > i[0] and i[1] > i[2] and i[1] > i[3]:\n",
    "        b.append(1)\n",
    "    elif i[2] > i[1] and i[2] > i[0] and i[2] > i[3]:\n",
    "        b.append(2)\n",
    "    else:\n",
    "        b.append(3)\n",
    "\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b9dcfe10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score :  0.76\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      "\n",
      " [[55  1  0  4]\n",
      " [ 2 72  0  9]\n",
      " [ 5  7  1  6]\n",
      " [ 4 10  0 24]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score : \",accuracy_score(y_test,b))\n",
    "cm = confusion_matrix(y_test, b)\n",
    "print(\"\\n\\nConfusion Matrix : \\n\\n\",cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "89819408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Cyst'),\n",
       " Text(0, 1.5, 'Normal'),\n",
       " Text(0, 2.5, 'Stone'),\n",
       " Text(0, 3.5, 'Tumor')]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax)\n",
    "\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(['Cyst', 'Normal','Stone','Tumor']) \n",
    "ax.yaxis.set_ticklabels(['Cyst', 'Normal','Stone','Tumor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "0965ef35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score is : 0.76\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score is :\",f1_score(y_test,b,average='micro'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9563eb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "pic = []\n",
    "image='resized dataset/Resized Folder 16x16/Cyst/Cyst- (1052).jpg'\n",
    "img = cv2.imread(str(image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "192b7288",
   "metadata": {},
   "outputs": [],
   "source": [
    "pic.append(img)\n",
    "pic1 = np.array(pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "69a9730a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n"
     ]
    }
   ],
   "source": [
    "a = model.predict(pic1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "db281d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kidney is \"Normal\" \n"
     ]
    }
   ],
   "source": [
    "if a.argmax() == 0 :\n",
    "    print(\"The detected disease is \\\"Cyst\\\"\")\n",
    "elif a.argmax() == 1 :\n",
    "    print(\"Kidney is \\\"Normal\\\" \")\n",
    "elif a.argmax() == 2:\n",
    "    print(\"The detected disease is \\\"Stone\\\"\")\n",
    "else:\n",
    "    print(\"The detected disease is \\\"Tumor\\\"\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}