WBC Abnormality Detection using Deep Learning

This project focuses on the automated detection of White Blood Cell (WBC) abnormalities related to blood cancer using advanced Deep Learning and Computer Vision techniques. Multiple Convolutional Neural Network (CNN) architectures were implemented, trained, and evaluated on microscopic blood cell images to classify:

ALL (Acute Lymphoblastic Leukemia) → Cancerous cells

HEM (Hematogones) → Normal cells

The study compares several state-of-the-art deep learning models to determine the most effective architecture for blood cancer detection.