RETRACTED ARTICLE: Enhanced firefly algorithm (EFA) based gene selection and adaptive neuro neutrosophic inference system (ANNIS) prediction model for detection of circulating tumor cells (CTCs) in breast cancer analysis

Umamaheswari, Thippaluru; Sumathi, P.

doi:10.1007/s10586-018-2183-2

Cited by 11 publications

(1 citation statement)

References 24 publications

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“…In the realm of medical diagnostics, the consequences of such discrepancies can be grave, given that a misdiagnosis can drastically alter a patient's treatment path. Moreover, while several studies highlight their methodological advancements [24][25][26], the accuracy rates of some models can still be improved. Given the critical nature of breast cancer diagnosis, achieving peak accuracy is of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning Approach for Breast Cancer Detection Based on DCNN

AlSalman,

Al-Rakhami,

Alfakih

et al. 2024

IEEE Access

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Breast cancer stands as one of the predominant health challenges globally, affecting millions of women every year and necessitating early and accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast cancer detection, their application is often hampered by privacy concerns associated with sharing patient data and the limitation of training on small, localized datasets. Addressing these challenges, this manuscript introduces an effective federated learning approach tailored for breast cancer detection, leveraging DCNNs across diverse and large datasets without compromising data privacy. Our experimental findings underscore significant advancements in detection accuracy of 98.9% on three large scale datasets which are VINDR-MAMMO, CMMD, and INBREAST. Additionally, we tested the proposed federated learning performance, showcasing the potential of our approach as a robust and privacy-preserving solution for future breast cancer diagnostic strategies.

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Section: Introductionmentioning

confidence: 99%