Melwin D. Souza, G. Ananth Prabhu, Varuna Kumara, K. M. Chaithra
{"title":"EarlyNet: a novel transfer learning approach with VGG11 and EfficientNet for early-stage breast cancer detection","authors":"Melwin D. Souza, G. Ananth Prabhu, Varuna Kumara, K. M. Chaithra","doi":"10.1007/s13198-024-02408-6","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02408-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
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.