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Code for the paper "Reinforced Active Learning for Image Segmentation", adapted to the medical domain for 3D anatomical and pathological segmentation.
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This work detects Malaria parasite in patients with the aim to assist physicians with speedy diagnosis.
This project is made from collecting knee digital xrays dateset to classify the state of the knee whether its normal state knee , doubtful state knee , mild state knee ,moderate state knee and severe state knee . -The initialization of the project starts by downloading the dataset digital knee xrays from mendeley which is available throughout this link : https://data.mendeley.com/datasets/t9ndx37v5h/1. -After exploring dataset we found that the dataset contains 5 main classes for knee state classification those 5 classes are : normal knee state , Doubtful , mild ,Moderate and severe knee classes -Fortunately the image data is cleaned it just needs resizing to size 224x224 in order to be used in more advanced CNNs such as VGG16 , MobileNet ... so on . -After Model training we need to save the model in Model saved format to be used later in deployment scenarios