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

• Published in: Al-Nahrain journal for engineering sciences Vol. 26; no. 2; pp. 57 - 62
• Main Authors: Alrubaie, Hiba, K. Aljobouri, Hadeel, J. AL-Jobawi, Zainab, Çankaya, Ilyas
• Format: Journal Article
• Language: English
• Published: Al-Nahrain Journal for Engineering Sciences 08.07.2023
• Subjects: Breast cancer; CNN; Feature Extraction; Medical Imaging; Ultrasound
• ISSN: 2521-9154; 2521-9162
• DOI: 10.29194/NJES.26020057

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%.