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A lightweight CNN for colon cancer tissue classification and visualization.

Jie Li¹, Weiwei Goh¹, Noor Zaman Jhanjhi¹

¹Digital Health and Medical Advancement Impact Lab, School of Computer Science, Taylor's University, Subang Jaya, Malaysia.

Frontiers in Oncology

|November 3, 2025

Summary

A new lightweight Convolutional Neural Network (CNN) model accurately classifies colon cancer (CC) histopathology images. This AI tool enhances diagnostic accuracy and efficiency in pathology, aiding future cancer detection.

Keywords:

CNN colon cancer data cleaning histopathology image processing lightweight model medical imaging

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Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Oncology

Background:

Colon cancer (CC) diagnosis relies heavily on histopathology image analysis.
Computational medical solutions are increasingly vital for modern radiology and pathology.
Accurate and efficient image classification is crucial for timely colon cancer detection.

Purpose of the Study:

To introduce a novel, lightweight Convolutional Neural Network (CNN) model for colon cancer histopathology image classification.
To develop an effective data cleaning strategy for improving image data quality.
To provide a resource-efficient model for colon cancer diagnostics.

Main Methods:

Development of a non-pretrained, lightweight CNN architecture.
Implementation of a parametric Gaussian distribution-based data cleaning method to remove outliers.
Training and validation using the NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets.

Main Results:

The model achieved a high test accuracy of 0.990 ± 0.003.
Demonstrated excellent precision, recall, specificity, and F1 scores across diverse tissue classes.
The lightweight model comprises 4,414,217 parameters and is 16.9 MB in size.

Conclusions:

The developed CNN model offers a robust and lightweight solution for colon cancer histopathology image classification.
The data cleaning strategy effectively enhances classification performance.
This research provides a foundation for advancing colon cancer diagnostics through AI and image visualization.