A novel and efficient statistical and soft-computing intelligence integrated feature selection technique for human chronic diseases prediction

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Язык: Английский

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Federated Learning for Secure and Privacy-Preserving Medical Diagnostics

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 155 - 186

Опубликована: Март 7, 2025

Federated learning is an emerging powerful approach for training ML models across decentralized data sources, such as in healthcare, without actually transferring the sensitive to a central server. Although FL naturally reduces risks of privacy since are confined local devices, it still can be highly vulnerable sophisticated attacks model inversion, poisoning, and inference attacks. Advanced privacy-preserving techniques that nowadays studied addressing security concerns this paper include differential privacy, homomorphic encryption, secure aggregation. These, therefore, open room adopted solution some privacy-sensitive domains. This conducts thorough review discussed outlines customized framework healthcare applications allow patient safeguarded detrimental effects on performance accuracy. Conclusion:

Язык: Английский

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An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

PLoS ONE, Год журнала: 2024, Номер 19(4), С. e0300622 - e0300622

Опубликована: Апрель 11, 2024

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast histological images an essential challenge automated pathology analysis. According to research, global BrC around 12% all cases. Furthermore, 25% women suffer from BrC. Consequently, prediction depends critically on quick precise processing imaging data. The primary reason deep learning models are used detection that they can produce findings more quickly accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated this work viability automatically classifying first stage pre-processing, which employs Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) remove high-density noise. After image has been pre-processed, it segmented using Thresholding Level set approach. Next, propose hybrid chaotic sand cat optimization technique, together with Remora Optimization Algorithm (ROA) for feature selection. suggested strategy facilitates acquisition functionality attributes, hence simplifying procedure. Additionally, aids resolving problems pertaining optimization. Following selection, best characteristics proceed categorization A DL classifier called Conditional Variation Autoencoder discriminate between cancerous benign tumors while categorizing them. classification accuracy 99.4%, Precision 99.2%, Recall 99.1%, F- score 99%, Specificity 99.14%, FDR 0.54, FNR 0.001, FPR 0.002, MCC 0.98 NPV 0.99 were obtained proposed compared other research results our desirable.

Язык: Английский

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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

BMC Medical Imaging, Год журнала: 2024, Номер 24(1)

Опубликована: Сен. 13, 2024

Язык: Английский

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FS-WOA-stacking: A novel ensemble model for early diagnosis of breast cancer

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 95, С. 106374 - 106374

Опубликована: Апрель 26, 2024

Язык: Английский

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Hybrid bio-inspired computing in medical image data analysis: A review

Intelligent Decision Technologies, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Авг. 30, 2024

Inspired by the fundamentals of biological evolution, bio-inspired algorithms are becoming increasingly popular for developing robust optimization techniques. These metaheuristic algorithms, unlike gradient descent methods, computationally more efficient and excel in handling higher order multi-dimensional non-linear. OBJECTIVES: To understand hybrid Bio-inspired domain Medical Imaging its challenges feature selection METHOD: The primary research was conducted using three major indexing database Scopus, Web Science Google Scholar. RESULT: included 198 articles, after removing 103 duplicates, 95 articles remained as per criteria. Finally 41 were selected study. CONCLUSION: We recommend that further area based field diagnostic imaging clustering. Additionally, there is a need to investigate use Deep Learning models integrating include strengths each enhances overall model.

Язык: Английский

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Optimizing Breast Cancer Diagnosis: Harnessing the Power of Nature-Inspired Metaheuristics for Feature Selection with Soft Voting Classifiers

International Journal of Cognitive Computing in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

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A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

PLoS ONE, Год журнала: 2024, Номер 19(5), С. e0303094 - e0303094

Опубликована: Май 20, 2024

In response to the growing number of diabetes cases worldwide, Our study addresses escalating issue diabetic eye disease (DED), a significant contributor vision loss globally, through pioneering approach. We propose novel integration Genetic Grey Wolf Optimization (G-GWO) algorithm with Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by Kernel Extreme Learning Machine (KELM) for refined image segmentation and classification. This innovative combination leverages genetic grey wolf optimization boost FCEDN’s efficiency, enabling precise detection DED stages differentiation among types. Tested across diverse datasets, including IDRiD, DR-HAGIS, ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% 98.8%, surpassing existing methods. advancement sets new standard in offers potential automating fundus analysis, reducing reliance on manual examination, improving patient care efficiency. findings are crucial enhancing diagnostic accuracy outcomes management.

Язык: Английский

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Revolutionizing breast cancer diagnosis with a comprehensive approach using digital mammogram-based feature extraction and selection for early-stage identification

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106268 - 106268

Опубликована: Апрель 16, 2024

Язык: Английский

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Efficient hybrid optimization based feature selection and classification on high dimensional dataset

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(20), С. 58689 - 58727

Опубликована: Дек. 23, 2023

Язык: Английский

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