Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

• Published in: PeerJ. Computer science Vol. 10; p. e1850
• Main Authors: Jakkaladiki, Sudha Prathyusha, Maly, Filip
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 28.02.2024; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Attention mechanism; Bioinformatics; Biopsy; Breast cancer; Cancer; Care and treatment; Classification; Computational linguistics; Computed tomography; Computer Vision; CT imaging; Data Mining and Machine Learning; Data Science; Datasets; Deep learning; Diagnosis; Diagnostic imaging; Efficiency; Feature fusion; Imaging systems; Language processing; Machine learning; Magnetic resonance imaging; Mammography; Medical diagnosis; Medical imaging; Medical prognosis; Medical screening; Methods; Natural language interfaces; Neural networks; Oncology, Experimental; Patients; Performance evaluation; Recall; Repositories; Thermography; Transfer learning; Tumors; Ultrasonic imaging; Womens health; X ray imagery; X-rays; Deep learning; Attention mechanism; Breast cancer; Feature fusion; Transfer learning
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1850

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system’s performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study’s findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.