From 0676db1aea8931c9bb230ae89e306c86b28c8024 Mon Sep 17 00:00:00 2001
From: Anjana Tiha <anjana.tiha@gmail.com>
Date: Thu, 14 Mar 2019 21:32:46 -0400
Subject: [PATCH] Update README.md

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-# Malaria-Detection-from-Cell-Images-using-Deep-Learning
-Malaria Detection from Cell Images using Deep Learning - NasNetMobile Model
+### Malaria Detection from Microscopic-Tissue Images with Deep Learning (Auto ML, Custom Convolutional Neural Network, NasNetMobile) 
+<pre>
+Domain             : Computer Vision, Machine Learning
+Sub-Domain         : Deep Learning, Image Recognition
+Techniques         : Deep Convolutional Neural Network, Transfer Learning, ImageNet, Auto ML, NASNetMobile
+Application        : Image Recognition, Image Classification, Medical Imaging
+</pre>
+
+### Description
+<pre>
+1. Detected Cancer from microscopic tissue images (histopathologic) with Auto ML (Google’s “NASNet”).
+2. For training, concatenated global pooling (max, average), dropout and dense layers to the output layer for final output prediction.
+3. Attained validation accuracy of 95.6% and loss 0.30 on 250K+ (6.5GB+) image cancer dataset.
+</pre>
+
+#### Code
+<pre>
+GitHub Link      : <a href=https://github.com/anjanatiha/Malaria-Detection-from-Cell-Images-using-Deep-Learning>Histopathologic Cancer Detection(GitHub)</a>
+GitLab Link      : <a href=https://gitlab.com/anjanatiha/Malaria-Detection-from-Cell-Images-using-Deep-Learnin>Histopathologic Cancer Detection(GitLab)</a>
+Portfolio        : <a href=https://anjanatiha.wixsite.com/website>Anjana Tiha's Portfolio</a>
+</pre>
+
+#### Dataset
+<pre>
+Dataset Name     : Malaria Cell Images Dataset
+Dataset Link     : <a href=https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria>Malaria Cell Images Dataset (Kaggle)</a>
+<!---                 
+Original Paper   : <a href=https://jamanetwork.com/journals/jama/fullarticle/2665774>Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer </a> 
+                   Authors: Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest 
+                   JAMA (The Journal of the American Medical Association)
+                   <cite>Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199–2210. doi:10.1001/jama.2017.14585</cite>
+</pre>
+-->
+
+### Dataset Details
+<pre>
+Dataset Name            : Malaria Cell Images Dataset
+Number of Class         : 2
+</pre>
+
+| Dataset Subtype | Number of Image | Size of Images (GB/Gigabyte) |
+| :-------------- | --------------: | ---------------------------: |
+| **Total**       | 27,588          | 337 MB                       |
+| **Training**    | 20,670          | - MB                         |
+| **Validation**  | 6,888           | - MB                         |
+| **Testing**     | -               | -                            |
+
+
+### Model and Training Prameters
+| Current Parameters   | Value                                                       |
+| :------------------- | ----------------------------------------------------------: |
+| **Base Model**       | NashNetMobile                                               |
+| **Optimizers**       | Adam                                                        |
+| **Loss Function**    | Categorical Crossentropy                                    |
+| **Learning Rate**    | 0.0001                                                      |
+| **Batch Size**       | 176                                                         |                                     
+| **Number of Epochs** | 10                                                          |
+| **Training Time**    | 45 Min                                                      |
+
+
+### Model Performance Metrics (Prediction/ Recognition / Classification)
+| Dataset              | Training       | Validation    | Test      |                                 
+| :------------------- | -------------: | ------------: | --------: |
+| **Accuracy**         | 96.47%         | 95.72%        | -         |
+| **Loss**             | 0.14           | 0.30          | -         |
+| **Precision**        | ---            | ---           | -         |
+| **Recall**           | ---            | ---           | -         |
+| **Roc-Auc**          | ---            | ---           | -         |
+
+
+### Other Experimented Model and Training Prameters
+| Parameters (Experimented) | Value                                                  |
+| :------------------------ | -----------------------------------------------------: |
+| **Base Models**           | NashNet(NashNetMobile)                                 |
+| **Optimizers**            | Adam                                                   |
+| **Loss Function**         | Categorical Crossentropy                               |
+| **Learning Rate**         | 0.0001, 0.00001, 0.000001, 0.0000001                   |
+| **Batch Size**            | 32, 64, 176                                            |                                     
+| **Number of Epochs**      | 10                                                     |
+| **Training Time**         | 45 Min                                                 |
+
+<!---
+##### Sample Output: 
+<kbd>
+<img src=https://github.com/anjanatiha/Histopathologic-Cancer-Detection/blob/master/demo/sample/sample.png>
+</kbd>
+<kbd>
+<a href=https://github.com/anjanatiha/Histopathologic-Cancer-Detection/blob/master/demo/images/result.png>See More Images</a>
+</kbd>
+##### Confusion Matrix: 
+<kbd>
+<img src=https://github.com/anjanatiha/Histopathologic-Cancer-Detection/blob/master/demo/report/CM.png alt="Confusion Matrix" width=800px height=600px>
+</kbd>
+-->
+#### Tools / Libraries
+<pre>
+Languages               : Python
+Tools/IDE               : Kaggle
+Libraries               : Keras, TensorFlow, NasNetMobile
+</pre>
+
+#### Dates
+<pre>
+Duration                : February 2019 - Current
+Current Version         : v1.0.0.9
+Last Update             : 03.14.2019
+</pre>