Early Detection and Diagnosis of Brain Related Diseases

Neurodegenerative disorders of the brain, including brain tumours, Parkinson’s, Alzheimer’s and dementia, present significant difficulties since they impair the cognitive and motor abilities of those who suffer from them. For the purpose of putting preventative measures into place and starting medical interventions on time, early detection and accurate diagnosis of these illnesses are essential. In order to aid in the early detection of these four brain-related diseases, this study suggests a novel strategy that uses speech signals processed and Brain Magnetic Resonance Imaging (MRI) data analysed via Computer Vision (CV) and Deep Learning approaches. Through the analysis of speech patterns and cognitive subtleties, the study seeks to find unique language indicators or anomalies associated with these neurodegenerative disorders. Meanwhile, the use of CV-based Deep Learning models will concentrate on obtaining complex features from brain MRI scans so as to identify abnormalities that are typical of these disorders in terms of both structure and function. By combining these two modalities, a thorough evaluation that combines neuroimaging data and verbal cues will be possible, providing a more accurate and comprehensive diagnostic framework. The methodology that has been suggested involves gathering data from a group of patients who have been diagnosed with different stages of the neurodegenerative disorders listed before, in addition to a control group of healthy individuals. Preprocessing, feature extraction, and fusion techniques will be applied to the collected data in order to create a strong prediction model. Neural networks and classification models are examples of machine learning methods that will be used to train and verify the CV framework for early disease detection.