Ray Team
Name: YINKA OLOWOFELA
Email Address: [email protected]
https://github.com/olowoyinka/ML_ImageClassification_ChestDisease
It is so obvious that an average PULMONOLOGIST spent several hour to diagnosis a sample image of X-Ray chest. Most especially, when diagnosis involves identifying the kind of disease that occurs in the chest.
Our project provides a Web based solution for this challenges faced by PULMONOLOGIST.
This solution visually inspect a sample of X-Ray chest images and predict the sample image according to the categories of Chest Disease.
Various categories of Chest Disease that could been predicted in this solution are Atelectasis, Infiltration, Effusion, Cardiomegaly and Fibrosis.
We leverage on Deep Learning Model using Transfer Learning, a Pretrained TensorFlow model and the ML.NET Image Classification API to train the model used for the predicting or classifying the sample images.
The Dataset for the training the model was found in Kaggle https://www.kaggle.com/yashprakash13/chest-xrays-dataset
https://drive.google.com/file/d/1GG1mWOi5AuK4qye-AHkMzEOHlfJ1XtYh/view?usp=sharing