Luying Zhang, Peng Zhang, Wen Chen
Overview of Patients with Hemophilia in China: Demographics, Diseases, Treatment, and Health Status
Patient preference and adherence
brain tumor classification using mri images crucial yet challenging task medical imaging accurate diagnosis vital effective treatment planning often hindered complex nature tumor morphology variations imaging traditional methodologies primarily rely manual interpretation mri images supplemented conventional machine learning techniques approaches often lack robustness scalability needed precise automated tumor classification major limitations include high degree manual intervention potential human error limited ability handle large datasets lack generalizability diverse tumor types imaging conditionsto address challenges propose federated learningbased deep learning model leverages power convolutional neural networks cnn automated accurate brain tumor classification innovative approach emphasizes use modified vgg16 architecture optimized brain mri images also highlights significance federated learning transfer learning medical imaging domain federated learning enables decentralized model training across multiple clients without compromising data privacy addressing critical need confidentiality medical data handling model architecture benefits transfer learning technique utilizing pretrained cnn significantly enhances ability classify brain tumors accurately leveraging knowledge gained vast diverse datasetsour model trained diverse dataset combining figshare sartaj br35h datasets employing federated learning approach decentralized privacypreserving model training adoption transfer learning bolsters models performance making adept handling intricate variations mri images associated different types brain tumors model demonstrates high precision 099 glioma 095 meningioma 100 tumor 098 pituitary recall f1scores classification outperforming existing methods overall accuracy stands 98 showcasing models efficacy classifying various tumor types accurately thus highlighting transformative potential federated learning transfer learning enhancing brain tumor classification using mri images