The comparative study of deep learning neural network approaches for breast cancer diagnosis

Mohd Nasir, Haslinah and Brahin, Noor Mohd Ariff and Zainuddin, Suraya and Mispan, Mohd Syafiq and Md Isa, Ida Syafiza and Sha’abani, Mohd Nurul Al Hafiz (2023) The comparative study of deep learning neural network approaches for breast cancer diagnosis. International Journal Of Online And Biomedical Engineering, 19 (6). pp. 127-140. ISSN 2626-8493

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Abstract

Breast cancer is one of the life-threatening cancer that leads to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) is evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN can deliver better results with the provided dataset. However, more improvement is needed for better performance to ensure that the approach used is reliable enough for early breast cancer diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Breast cancer, Early diagnosis, Deep learning, Prognosis, Neural network
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	04 Jul 2024 12:16
Last Modified:	04 Jul 2024 12:16
URI:	http://eprints.utem.edu.my/id/eprint/27492
Statistic Details:	View Download Statistic

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