# Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

arXiv: https://arxiv.org/abs/2009.03878 · HTML: https://mlsanity.com/paper/2009.03878/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning](https://mlsanity.com/paper/2206.01088/) (2022)
- [Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets](https://mlsanity.com/paper/2210.00824/) (2022)
- [Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification](https://mlsanity.com/paper/2405.04610/) (2024)
- [Multi-Scale Deep Learning for Colon Histopathology: A Hybrid Graph-Transformer Approach](https://mlsanity.com/paper/2509.02851/) (2025)
- [Lung Cancer Classification from CT Images Using ResNet](https://mlsanity.com/paper/2510.16310/) (2025)

## Abstract (arXiv)

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.