Breast cancer classification with histopathological image based on machine learning - CNN model - transfer learning

Aug 5, 2026

Upload by Anda

Breast cancer classification is addressed by a convolutional neural network (CNN) deep learning framework that distinguishes histopathology images as benign or malignant to support earlier, more effective treatment. The approach evaluates five pre-trained CNN architectures, including ResNet-50, VGG-19, Inception-V3, and AlexNet, with ResNet-50 also used as a feature extractor. Extracted features are further classified using random forest (RF) and k-nearest neighbors (KNN). Experiments use the BreakHis public dataset, achieving up to 97% test accuracy with ResNet-50.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Proposed CNN-based classification approach

Related Work

Prior CNN, DBN, and GoogLeNet/Inception methods

Transfer learning and dataset augmentation

Performance and accuracy results

Breast cancer classification with histopathological image based on machine learning Jia Rong Leow, Wee How Khoh, Ying Han Pang, Hui Yen Yap Faculty of Information Science and Technology, Multimedia University, Malacca, Malaysia Article history: Received Nov 15, 2022 Revised Mar 29, 2023 Accepted Apr 7, 2023 Keywords: Breast cancer classification Convolution neural network Image processing Machine learning Transfer learning Corresponding Author: Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images. This is an open access article under the CC BY-SA license. Wee How Khoh Faculty of Information Science and Technology, Multimedia University St. Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia [Email: whkhoh@mmu.edu.my](Email: whkhoh@mmu.edu.my) Article Info ABSTRACT 1. INTRODUCTION Breast cancer has a high death rate [1] . However, nowadays the chances of cure are excellent with the ongoing advancement of advanced treatment levels and the upgrading of equipment. Current breast-conserving surgery has a therapeutic result comparable to major resection and modified radical surgery, which not only protects the structural integrity of women ’s breasts but also reduces their physical and psychological trauma. [2] . Early identification of breast cancer has a greater survival percentage than medium and late stages. Due to the fact that the cancer cells in the early stages of breast cancer have not disseminated, they are amenable to treatment with local surgery, radiation, chemotherapy, hormone therapy, and other comprehensive treatments with a very high cure rate. Current medical technology and pharmaceuticals for treating breast cancer have advanced significantly in comparison to the past, as it has the rate of early identification of breast cancer. As a result, early diagnosis of breast cancer may boost the success rate of therapy and assist to decrease the death rate of breast cancer patients [3] . This work proposes the use of pre-trained convolutional neural network (CNN) algorithms for distinguishing breast cancer histological images of patients. BreakHis is the dataset that employed in this study. This paper also demonstrateshow the CNN performance could be mixed with machine learning methods for classification. Finally, a state-of-art result of the models to categorize breast cancer as benign or malignant, and compared the performances ofall of the pre-trained models that are employed. 2. RELATED WORK Reza and Ma [4] employed CNN to classify imbalanced data from histopathological photos of malignant and non-cancerous tissues. The dataset is heavily imbalanced since the malignant class image isabout 3 times as much as the benign class. The oversampling and under-sampling methods were used to address this imbalance problem. Under sampling resamples the data by eliminating majority classes until the min

FAQs

What is the main goal of the proposed work?

To classify breast cancer histopathological images into benign or malignant using a CNN-based deep learning model to improve early detection and treatment decisions.

Which CNN architectures are evaluated in the study?

ResNet-50, VGG-19, Inception-V3, and AlexNet are used as pre-trained CNN models, and their performances are compared for the classification task.

How is ResNet-50 used beyond end-to-end CNN classification?

ResNet-50 serves as a feature extractor, and the extracted information is passed to machine learning classifiers including random forest (RF) and k-nearest neighbors (KNN).

What dataset and top performance does the paper report?

Experiments are conducted on the BreakHis public dataset, where ResNet-50 achieves the highest test accuracy of 97% for classifying breast cancer images.

Aug 5, 2026

Upload by Anda