A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos DOI Creative Commons
Shagun Sharma, Kalpna Guleria, Ayush Dogra

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0316543 - e0316543

Published: Feb. 11, 2025

Malignant glioma is the uncontrollable growth of cells in spinal cord and brain that look similar to normal glial cells. The most essential part nervous system cells, which support brain’s functioning prominently. However, with evolution glioma, tumours form invade healthy tissues brain, leading neurological impairment, seizures, hormonal dysregulation, venous thromboembolism. Medical tests, including medical resonance imaging (MRI), computed tomography (CT) scans, biopsy, electroencephalograms are used for early detection glioma. these tests expensive may cause irritation allergic reactions due ionizing radiation. deep learning models highly optimal disease prediction, however, challenge associated it requirement substantial memory storage amalgamate patient’s information at a centralized location. Additionally, also has patient data-privacy concerns anonymous generalization, regulatory compliance issues, data leakage challenges. Therefore, proposed work, distributed privacy-preserved horizontal federated learning-based malignant model been developed by employing 5 10 different clients’ architectures independent identically (IID) non-IID distributions. Initially, developing this model, collection MRI scans non-tumour done, further pre-processed performing balancing image resizing. configuration development pre-trained MobileNetV2 base have performed, then applied learning(FL) framework. configurations kept as 0.001, Adam, 32, 10, FedAVG, rate, optimizer, batch size, local epochs, global aggregation, rounds, respectively. provided prominent accuracy architecture 99.76% 99.71% IID distributions, These outcomes demonstrate optimized generalizes improved when compared state-of-the-art models.

Language: Английский

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos DOI Creative Commons
Shagun Sharma, Kalpna Guleria, Ayush Dogra

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0316543 - e0316543

Published: Feb. 11, 2025

Malignant glioma is the uncontrollable growth of cells in spinal cord and brain that look similar to normal glial cells. The most essential part nervous system cells, which support brain’s functioning prominently. However, with evolution glioma, tumours form invade healthy tissues brain, leading neurological impairment, seizures, hormonal dysregulation, venous thromboembolism. Medical tests, including medical resonance imaging (MRI), computed tomography (CT) scans, biopsy, electroencephalograms are used for early detection glioma. these tests expensive may cause irritation allergic reactions due ionizing radiation. deep learning models highly optimal disease prediction, however, challenge associated it requirement substantial memory storage amalgamate patient’s information at a centralized location. Additionally, also has patient data-privacy concerns anonymous generalization, regulatory compliance issues, data leakage challenges. Therefore, proposed work, distributed privacy-preserved horizontal federated learning-based malignant model been developed by employing 5 10 different clients’ architectures independent identically (IID) non-IID distributions. Initially, developing this model, collection MRI scans non-tumour done, further pre-processed performing balancing image resizing. configuration development pre-trained MobileNetV2 base have performed, then applied learning(FL) framework. configurations kept as 0.001, Adam, 32, 10, FedAVG, rate, optimizer, batch size, local epochs, global aggregation, rounds, respectively. provided prominent accuracy architecture 99.76% 99.71% IID distributions, These outcomes demonstrate optimized generalizes improved when compared state-of-the-art models.

Language: Английский

Citations

0