Markos Wondim Walle; Kula Kakeba Tune; Natnael Tilahun Sinshaw; Sudhir Kumar Mohapatra

Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network

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Author(s)

Markos Wondim Walle ¹ Kula Kakeba Tune ¹ Natnael Tilahun Sinshaw ¹ Sudhir Kumar Mohapatra ^2,*

1. Department of Software Engineering, CoE for HPC and BDA, AASTU, Addis Ababa, Ethiopia

2. Faculty of Emerging Technologies, Sri Sri University, Cuttack, Odisha, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2023.04.04

Received: 7 Jan. 2023 / Revised: 12 Mar. 2023 / Accepted: 28 Apr. 2023 / Published: 8 Aug. 2023

Index Terms

Deep Learning, Convolutional Neural Network, Breast Cancer Classification, Benign, Malignant

Abstract

Breast cancer is a leading cause of death among women, and the subjectivity of human visual perception and lack of automated detection methods can lead to misclassification of breast cancer images. In this study, a breast cancer classification model using a Convolutional Neural Network (CNN) deep learning algorithm was proposed. The model demonstrated high accuracy in classifying breast images as benign or malignant, with a classification accuracy of 97.1%. The model was also able to run on low computational resources. The study used a dataset of 2009 breast images labeled by two radiologists and included six scenarios based on different hyperparameters, augmentation values, pretrained models, and models built from scratch. While the performance of the proposed model was promising, further improvement may be achieved by using a larger breast image dataset and a machine with more powerful GPU hardware.

Cite This Paper

Markos Wondim Walle, Kula Kakeba Tune, Natnael Tilahun Sinshaw, Sudhir Kumar Mohapatra, "Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network", International Journal of Engineering and Manufacturing (IJEM), Vol.13, No.4, pp. 34-43, 2023. DOI:10.5815/ijem.2023.04.04

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