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A Hybrid Lung and Colon Histopathological Image Classification Framework Using MobileNetV3-Small Deep Features and

Muhammad Usama Naveed¹, Sohail Jabbar², Muhammad Munwar Iqbal¹

¹Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan.

Diagnostics (Basel, Switzerland)

|May 13, 2026

Summary

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This summary is machine-generated.

This study introduces an automated framework for diagnosing lung and colon cancer from histopathological images, achieving 98.14% accuracy using deep learning and feature optimization for efficient clinical application.

Area of Science:

Medical Image Analysis
Computational Pathology
Artificial Intelligence in Oncology

Background:

Cancer, particularly lung and colon cancer, is a major global health concern.
Traditional histopathological diagnosis is labor-intensive, requires specialized expertise, and is prone to errors.
Automated methods are needed to improve the efficiency and accuracy of cancer diagnosis.

Purpose of the Study:

To develop an automated classification framework for lung and colon cancer detection using histopathological images.
To leverage a lightweight deep learning model (MobileNetV3-Small) for efficient feature extraction.
To optimize extracted features for enhanced classification performance and computational speed.

Main Methods:

Utilized transfer learning with the MobileNetV3-Small deep learning model on an enhanced LC25000 dataset.

Keywords:

classification deep learning feature optimization histopathological imaging lung and colon cancer transfer learning

Related Experiment Videos

Resized histopathological images to 224 × 224 × 3 pixels for model compatibility.

Extracted deep features from the dropout layer and optimized them using a differential evolution algorithm, reducing dimensionality.

Evaluated optimized features using various classifiers, including Quadratic Support Vector Machine (SVM) and bagged trees.

Main Results:

Achieved a maximum classification accuracy of 98.14% with a Quadratic SVM.
Demonstrated a 21.3× speed-up in classification using bagged trees.
Outperformed several state-of-the-art methods, showing a 3.34% improvement over the baseline on the enhanced dataset.

Conclusions:

The proposed framework effectively balances high diagnostic accuracy with computational efficiency.
The combination of a lightweight deep learning model and feature optimization is suitable for practical clinical settings.
This automated approach offers a promising tool for improving cancer diagnosis workflows.