Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

TL;DR

This study develops a convolutional neural network-based system to accurately diagnose lung and colon cancers from histopathological images, achieving over 96% accuracy, demonstrating AI's potential in medical diagnostics.

Contribution

The paper introduces a CNN-based computer-aided diagnosis system for lung and colon cancers with high accuracy, using a large dataset of digital pathology images.

Findings

Diagnostic accuracy over 97% for lung cancer

Diagnostic accuracy over 96% for colon cancer

Effective classification of cancer types using shallow neural networks

Abstract

Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 dataset containing 5000 images for each class. A shallow neural network architecture was used classify the histopathological slides into squamous cell carcinomas, adenocarcinomas and benign for the lung. Similar model was used to classify adenocarcinomas and benign for colon. The diagnostic accuracy of more than 97% and 96% was recorded for lung and colon respectively.