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Updated: Aug 24, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.

Musa Adamu Wakili¹, Harisu Abdullahi Shehu², Md Haidar Sharif³

¹Abubakar Tafawa Balewa University, Bauchi 740272, Nigeria.

Computational Intelligence and Neuroscience

|October 20, 2022

Summary

This summary is machine-generated.

A new deep learning method, DenTnet, accurately classifies breast cancer histopathological images using transfer learning. This approach improves detection accuracy and computational speed, addressing limitations of existing models.

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Area of Science:

Oncology
Computer Science
Medical Imaging

Background:

Breast cancer diagnosis from histopathological images is complex and prone to expert disagreement.
Deep learning models show promise but often suffer from high computational costs and overfitting.
Existing methods may struggle with feature extraction from similar data distributions.

Purpose of the Study:

To survey deep learning models for histopathological image classification and identify optimal training-testing ratios.
To propose an efficient and accurate deep learning method for breast cancer classification.
To overcome computational expense and feature distribution limitations of current methods.

Main Methods:

Conducted a survey on deep learning models for histopathological image classification.
Investigated popular and optimized training-testing ratios, finding 80%:20% yields best performance.
Developed DenTnet, a transfer learning-based model using DenseNet as a backbone.

Main Results:

The 80%:20% training-testing ratio demonstrated superior performance compared to 70%:30%.
DenTnet achieved up to 99.28% detection accuracy on the BreaKHis dataset.
The proposed method exhibited good generalization ability and computational efficiency.

Conclusions:

DenTnet effectively classifies breast cancer histopathological images with high accuracy and speed.
Transfer learning mitigates issues of high computation and feature distribution limitations.
The 80%:20% training-testing ratio is optimal for histopathological image classification tasks.