This project uses YOLO for real-time leukemia detection in blood samples and CNNs for classifying brain hemorrhages in MRI scans. It aims to support faster, more accurate medical diagnostics through deep learning.

This project uses YOLO for real-time leukemia detection in blood samples and CNNs for classifying brain hemorrhages in MRI scans. It aims to support faster, more accurate medical diagnostics through deep learning.

A web app that uses a custom-built Keras model to recognize and classify white blood cells from microscopic images. It can distinguish between different types of WBCs and identify artifacts and bursted cells. I created this app as a way to explore image recognition technologies and to learn how to develop Keras models from scratch.

Automated blood sample image analysis using computer vision and machine learning. Detects and segments blood cells, extracts features, performs anomaly detection, and provides statistical and clustering analysis of cell populations. Includes Jupyter notebooks for experimentation, parameter tuning, and comparative analysis of multiple samples