README.md

Malaria Detection from Microscopic-Tissue Images with Deep Learning (Auto ML, Custom Convolutional Neural Network, NasNetMobile)

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

Description

1. Detected Malaria from microscopic tissue images 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 95.72% and loss 0.1385 on 27K+ (330MB+) image cancer dataset.

Code

GitHub Link      : Malaria Detection using Deep Learning (GitHub)
GitLab Link      : Malaria Detection using Deep Learning (GitLab)
Kaggle Link      : malaria-detection-using-keras-accuracy-95
Portfolio        : Anjana Tiha's Portfolio

Dataset

Dataset Name     : Malaria Cell Images Dataset
Dataset Link     : Malaria Cell Images Dataset (Kaggle)

Dataset Details

Dataset Name            : Malaria Cell Images Dataset
Number of Class         : 2

Dataset Subtype	Number of Image	Size(MB/Megabyte)
Total	27,588	337 MB
Training	20,670	MB
Validation	6,888	MB
Testing

Dataset Subtype	Number of Image	Size of Images (GB/Gigabyte)
Total	27,588	337 MB
Training	20,670
Validation	6,888
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	0.1025	0.1385	-
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

Tools / Libraries

Languages               : Python
Tools/IDE               : Kaggle
Libraries               : Keras, TensorFlow, NasNetMobile

Dates

Duration                : February 2019 - Current
Current Version         : v1.0.0.9
Last Update             : 03.14.2019