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 250K+ (6.5GB+) 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)

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