Multi-class Breast Cancer Classification Using CNN Features Hybridization

Chakravarthy, Sannasi; Bharanidharan, N.; Khan, Surbhi Bhatia; Kumar, V. Vinoth; Mahesh, T. R.; Almusharraf, Ahlam; Albalawi, Eid

Abstract

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.

Citation

Chakravarthy, S., Bharanidharan, N., Khan, S. B., Kumar, V. V., Mahesh, T. R., Almusharraf, A., & Albalawi, E. (2024). Multi-class Breast Cancer Classification Using CNN Features Hybridization. International Journal of Computational Intelligence Systems, 17, Article 191. https://doi.org/10.1007/s44196-024-00593-7

• Journal Article Type: Article
• Acceptance Date: Jul 5, 2024
• Online Publication Date: Jul 22, 2024
• Publication Date: Jul 22, 2024
• Deposit Date: Aug 6, 2024
• Publicly Available Date: Aug 6, 2024
• Journal: International Journal of Computational Intelligence Systems
• Print ISSN: 1875-6891
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Article Number: 191
• DOI: https://doi.org/10.1007/s44196-024-00593-7
• Keywords: Breast cancer, Transfer learning, Mammogram images, Feature fusion, Deep neural networks, Late fusion