Abstract: Brain tumors are some of the most serious issues affecting the central nervous system. They need to be detected early and correctly. Traditional machine learning techniques rely on collecting data in one place, which raises significant privacy and security concerns. This work proposes a federated learning (FL)-based framework for classification of tumors from MRI, enabling decentralized training while preserving patient confidentiality. Multiple healthcare institutions collectively train deep learning models (VGG16, DenseNet) using the Federated Averaging algorithm, where only the model parameters are shared with a central server. The workflow includes preprocessing, feature extraction, local training, aggregation, and evaluation. Brain tumors are classified into glioma, pituitary, meningioma, and no-tumor classes. Experimental results are measured by precision, accuracy, recall, and F1-score and demonstrate robustness and strong classification performance. The paper highlights the scalability and usability of FL in medical imaging and introduces a secure federated approach to facilitate AI-driven healthcare applications.
Brain tumors are among the most life-threatening neurological disorders, and their early, accurate diagnosis through Magnetic Resonance Imaging (MRI) is critical for effective treatment planning. Manual interpretation of MRI scans is time-consuming, subjective, and prone to inter…
The automatic multiclass brain tumor classification using MRI images plays an important role in a non-invasive clinical setting. However, the choice of a model demands the trade-off between accuracy, complexity, and interpretability of the classifier. In this study, we have estab…
Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architecture diversity, priv…
Image copy detection is commonly addressed using either local descriptors or deep learning models, which can be computationally expensive and rely on high-dimensional features. In contrast, this work explores copy detection using compact perceptual hash representations and learne…
Source Code for: Detection of Brain Space-Occupying Lesions Using Quantum Machine Learning", "description": "Source code for the four-phase deep-learning pipeline presented in Amin, J., Anjum, M. A., Gul, N., & Sharif, M. (2023), 'Detection of brain space-occupying lesions using…
Background and Objective: Self-harm is a psychologically damaging behavior, and its accurate differentiation from other wounds (violence, accidents, burns, diabetic ulcers) is critically important in forensic medicine. However, this differentiation often falls into a diagnostic "…