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🔬This project aims to diagnose lung and colon cancer using histopathological images. It utilizes deep learning techniques, specifically convolutional neural networks (CNNs), to classify images into different cancer types and benign tissues.

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mohamedbashir/Diagnosis-Lung-and-Colon-Cancer-Using-Histopathological-Images

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Dataset Overview

This dataset consists of 25,000 color images, categorized into five distinct classes with 5,000 images per class. Each image is 768 x 768 pixels in size and stored in JPEG format. The main directory, lung_colon_image_set, contains two subdirectories: colon_image_sets and lung_image_sets.

  • The colon_image_sets folder includes:

    • colon_aca: 5,000 images of colon adenocarcinoma tissues.
    • colon_n: 5,000 images of benign colon tissues.
  • The lung_image_sets folder includes:

    • lung_aca: 5,000 images of lung adenocarcinoma tissues.
    • lung_scc: 5,000 images of lung squamous cell carcinoma tissues.
    • lung_n: 5,000 images of benign lung tissues.

We processed each cancer type individually and used the splitfolders function to split the dataset into training (70%), testing (20%), and validation (10%) sets.

Dataset

Dataset source: Kaggle - Lung and Colon Cancer Histopathological Images


How to Run the Project

  1. Install the required packages by running the following command:

    pip install -r requirements.txt
  2. Navigate to the directory where the server.py file is located:

    cd /path/to/server.py
  3. Start the server:

    python server.py
  4. Access the API documentation in your browser:


Screenshots

Lung API Interface

LungAPI

Colon API Interface

ColonAPI


About

🔬This project aims to diagnose lung and colon cancer using histopathological images. It utilizes deep learning techniques, specifically convolutional neural networks (CNNs), to classify images into different cancer types and benign tissues.

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