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 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
0.1025
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