Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction

Abstract

With the escalating incidence of brain tumors, early and accurate detection holds paramount importance for facilitating timely medical interventions and enhancing patient outcomes. Deep learning models have proven to be formidable tools for analyzing intricate medical data, particularly in the context of medical imaging. This review encompasses a meticulous examination of various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, as they are employed across diverse medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). The paper delves into the distinctive challenges inherent in brain tumor prediction, acknowledging factors such as inherent tumor variability and the necessity for models that are interpretable and applicable within clinical settings. Additionally, the review explores the fusion of multimodal data and the integration of transfer learning and domain adaptation techniques to enhance model generalization across diverse datasets. A critical evaluation of the strengths and limitations of current methodologies is provided, offering insights into potential avenues for future research. Moreover, this paper provides an extensive overview of deep learning methodologies applied to the prediction of brain tumors, exploring recent advancements and confronting challenges in this pivotal domain of medical research