Citation

Leow, Jia Rong and Khoh, Wee How and Pang, Ying Han and Yap, Hui Yen (2023) Breast cancer classification with histopathological image based on machine learning. International Journal of Electrical and Computer Engineering (IJECE), 13 (5). p. 5885. ISSN 2088-8708

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Breast cancer classification Convolution neural network Image processing Machine learning Transfer learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	28 Jul 2023 02:14
Last Modified:	28 Jul 2023 02:14
URII:	http://shdl.mmu.edu.my/id/eprint/11555

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