Published Paper Details:

Neurological and behavioral symptoms arising from medical conditions can significantly impact daily life due to their influence on brain structure and function. Early and accurate detection of these conditions is crucial, prompting significant interest in the application of computer-aided algorithms for diagnosis. Recent advancements in artificial intelligence (AI), particularly deep learning, offer novel solutions. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are being explored for their potential to analyze medical imaging data, such as Magnetic Resonance Imaging (MRI) scans, in the identification of these conditions. Deep learning algorithms demonstrate promising results with high accuracy. The accuracy rates for certain architectures, such as 18-layer CNN models, have been reported to exceed 98% in studies utilizing the powerful computer systems equipped with CUDA-enabled GPUs and large data sets of MRI scans from initiatives like Alzheimer's Disease Neuroimaging Initiative (ADNI). These results indicate that deep learning-based methods have the potential to improve and expedite medical diagnoses, paving the way for advancements in patient care.