EarlyNet: a novel transfer learning approach with VGG11 and EfficientNet for early-stage breast cancer detection

Melwin D. Souza (), G. Ananth Prabhu, Varuna Kumara and K. M. Chaithra
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Melwin D. Souza: Sahyadri College of Engineering & Management
G. Ananth Prabhu: Sahyadri College of Engineering & Management
Varuna Kumara: Moodlakatte Institute of Technology
K. M. Chaithra: SJB Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 8, No 33, 4018-4031

Abstract: Abstract Early-stage breast cancer detection remains a critical challenge in healthcare, demanding innovative approaches that leverage the power of deep learning and transfer learning techniques. The problem to be investigated involves designing a model capable of extracting meaningful features from mammographic images, maximizing transferability across datasets, and optimizing the trade-off between model complexity and computational efficiency. Existing methods often face limitations in achieving high accuracy, robustness, and efficiency. This research aims to address these challenges by proposing a novel transfer learning approach that combines the strengths of VGG11 and EfficientNet architectures for early-stage breast cancer detection. In the case of technological development, there is never a shortage of opportunities in the field of medical imaging. Cancer patients who have an earlier diagnosis of their disease have a lower probability of passing away from their illness. This research proposed an novel early neural network based on transfer learning names as ‘EARLYNET’ to automate breast cancer prediction. In this research, the new hybrid deep learning model was devised and built for distinguishing benign breast tumors from malignant ones. The trials were carried out on the Breast Histopathology Image dataset, and the model was evaluated using a Mobile net founded on the transfer learning method. In terms of accuracy, this model delivers 91.53% accuracy. Explored how the proposed transfer learning framework can enhance the accuracy and reliability of early-stage breast cancer detection, contributing to advancements in medical image analysis and positively impacting patient outcomes.

Keywords: Deep learning; Deep neural network; Image processing; Breast cancer detection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02408-6

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