Affiliations: [a] National Engineering School of Sfax, University of Sfax, Sfax, Tunisia | [b] Laboratory of Electrical Engineering (LAGE), University of KASDI Merbah Ouargla, Ouargla, Algeria | [c] National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia | [d] Artifcial Intelligence and Information Technology Laboratory (LINATI), Kasdi Merbah University, Ouargla, Algeria | [e] Faculty of Sciences, University of Sfax, Sfax, Tunisia | [f] Advanced Technologies for Environment and Smart Cities (ATES Unit), University of Sfax, Sfax, Tunisia
Correspondence:
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Corresponding author: Mouhamed Laid Abimouloud, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia. E-mail: mohamed.abimouloud@enis.tn.
Abstract: Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women’s health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time.
Keywords: Breast cancer (BC), convolutional networks (CNN), computer-aided diagnosis (CAD), vision transformers (VIT), mammography, DDSM
