Lung and Colon Caner Detection Using ResNet-50 on Histopathology Images

This repository contains a deep learning project for detecting lung and colon cancer from histopathology images using the ResNet-50 architecture. The goal is to classify images as either normal or cancerous to aid in the early diagnosis of lung and colon cancer.

Table of Contents

• Introduction
• Dataset
• Installation
• Model Architecture
• Training
• Evaluation
• Contribution

Introduction

Histopathology is the study of the microscopic structure of tissues, and it is a crucial method for diagnosing various types of cancers. This project leverages deep learning, specifically a ResNet-50 model; To automate the detection of lung and colon cancer from histopathology imgages. By accurately classifying these images, the model can assist pathoogists in diagnosing cancer more efficiently and accurately.

Dataset

The dataset used in this project consists of histopathology images from lung and colon tissues. The dataset is divided into training, validation and test set.

dataset link - https://academictorrents.com/details/7a638ed187a6180fd6e464b3666a6ea0499af4af

Installation

To run this project, you need to have Python and several Python libraries installed. You can install the required libraries using the following command:

pip install -r requirements.txt

Model Architecture

The model is based on the ResNet-50 architecture, a deep residual network that is widely used for image classification tasks. I fine-tuned the ResNet-50 model on the histopathology dataset.

Training

The model is trained using the following hyperparameters:

• Optimizer: Adam
• Loss Function: Categorical Crossentropy
• Epochs: 60

Evaluation

The model's performance is evaluated using accuracy, precision, recall, and F1-Score.

Contribution

Contribution are welcome! Please open an issue or submit a pull request if you have any suggestion or improvements.