Speaker
Description
This presentation explores the statistical challenges and comparative performance of various deep learning models for the automated detection and classification of neurological diseases from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Building upon initial findings that demonstrated the potential of Convolutional Neural Networks (CNNs) in recognizing rare brain pathologies, such as Fahr's disease, this Master's research aims to develop a robust system capable of identifying a spectrum of conditions, ranging from common disorders (e.g., stroke, brain cancer, Alzheimer's disease) to less prevalent ones (e.g., Moyamoya disease).
A primary focus of this work lies in the statistical rigor of model evaluation, especially in imbalanced data settings characteristic of rare diseases. The study will employ and statistically compare the efficacy of transfer learning approaches with custom-built CNN architectures (e.g., VGG, ResNet) using metrics particularly sensitive to low-prevalence classes, such as F1-score and Precision-Recall curves. Methodological details will be provided on the strategies used for dataset construction, augmentation, and bias mitigation - crucial steps in ensuring model generalizability and reliability.
The research culminates in a comparative statistical analysis to determine the optimal CNN strategy for this challenging, multi-class classification problem, underscoring the potential of advanced statistical modeling and machine learning in improving diagnostic speed and increasing awareness of challenging neurological conditions in preventive healthcare.
96432311448