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

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-07-04 DOI:10.1007/s13198-024-02408-6
Melwin D. Souza, G. Ananth Prabhu, Varuna Kumara, K. M. Chaithra
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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.

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EarlyNet:利用 VGG11 和 EfficientNet 检测早期乳腺癌的新型迁移学习方法
早期乳腺癌检测仍然是医疗保健领域的一项重要挑战,需要利用深度学习和迁移学习技术的力量来开发创新方法。需要研究的问题包括设计一种能够从乳腺X光图像中提取有意义特征的模型,最大限度地提高跨数据集的可转移性,以及优化模型复杂性和计算效率之间的权衡。现有方法在实现高准确性、稳健性和高效性方面往往面临局限。本研究旨在通过提出一种新型迁移学习方法来应对这些挑战,该方法结合了 VGG11 架构和 EfficientNet 架构在早期乳腺癌检测方面的优势。就技术发展而言,医学影像领域从来不缺少机遇。癌症患者如果能更早地诊断出自己的疾病,就能降低因病去世的概率。这项研究提出了一种基于迁移学习的新型早期神经网络,命名为 "EARLYNET",用于自动预测乳腺癌。这项研究设计并建立了新的混合深度学习模型,用于区分良性乳腺肿瘤和恶性乳腺肿瘤。试验在乳腺组织病理学图像数据集上进行,并使用基于迁移学习方法的移动网络对模型进行了评估。就准确率而言,该模型的准确率为 91.53%。探讨了所提出的迁移学习框架如何提高早期乳腺癌检测的准确性和可靠性,从而推动医学图像分析的进步,并对患者的治疗效果产生积极影响。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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