Open Access

ARTICLE

ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

by Nishant Behar^*, Manish Shrivastava

Department of Computer Science & Engineering, GGV, Bilaspur, 495009, India

* Corresponding Author: Nishant Behar. Email:

(This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)

Computer Modeling in Engineering & Sciences 2022, 130(2), 823-839. https://doi.org/10.32604/cmes.2022.017030

Received 26 April 2021; Accepted 11 August 2021; Issue published 13 December 2021

Abstract

Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer. This study proposes an effective convolutional neural network-based (CNN-based) model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour, using histopathology images. Resnet50 architecture has been trained on new dataset for feature extraction, and fully connected layers have been fine-tuned for achieving highest training, validation and test accuracies. The result illustrated state-of-the-art performance of the proposed model with highest training, validation and test accuracies as 99.70%, 99.24% and 99.24%, respectively. Classification accuracy is increased by 0.66% and 0.2% when compared with similar recent studies on training and test data results. Average precision and F1 score have also improved, and receiver operating characteristic (RoC) area has been achieved to 99.1%. Thus, a reliable, accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

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