Convolutional Neural Network Deep Learning Model for Improved Ultrasound Breast Tumor Classification

Hiba Alrubaie; Hadeel K. Aljobouri; Zainab  J. AL-Jobawi; Ilyas Çankaya

doi:10.29194/NJES.26020057

Authors

Hiba Alrubaie 1) Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
Hadeel K. Aljobouri Department of Biomedical Engineering, College of Engineering, Al-Nahrain University
Zainab J. AL-Jobawi Ministry of Health, Babil Governorate Health Directorate, Imam Al-Sadiq Teaching Hospital, Babylon, Iraq.
Ilyas Çankaya Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, 06010 Ankara, Turkey

DOI:

https://doi.org/10.29194/NJES.26020057

Keywords:

Breast cancer, CNN, Ultrasound, Feature Extraction, Medical Imaging

Abstract

Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.

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