Blood Cell Cancer Classification

Introduction

This project involves the use of a deep learning model, ResNet, VGG, to classify blood cell cancer images into two primary classes: Benign and Malignant. The malignant class is further divided into three subtypes: Pre-B, Pro-B, and early Pre-B. This document provides an in-depth explanation of the data processing, model architecture, training, evaluation, and inference used in this project. The ResNet and VGG architectures used in this project consists of multiple convolutional layers, pooling layers, and fully connected layers, allowing for effective feature extraction from complex medical images.