Early Prediction of Neurological Disorders using Automatic Deep Feature Extraction and Machine Learning
The focus of recent research has been on using advanced computer-aided diagnostic (CAD) techniques and a variety of modalities to identify neurological disorders. Important and possibly deadly conditions, neurological diseases such as Alzheimer's disease (AD), stroke, epilepsy, Parkinson's disease (PD), cerebral palsy, autism, and schizophrenia (SZ) often result in more serious health issues. CAD systems for neurological diseases using a variety of modalities have attracted a lot of interest due to different approaches of Artificial Intelligence (AI) like Deep Learning (DL) and Machine Learning (ML). However, the difficulty of automatically predicting many neurological disorders using neuroimaging data remains unresolved due to the lack of a comprehensive and highly accurate solution. A novel approach is presented in which an automated deep feature extraction along with machine learning classifiers were used for detection neurological diseases (for instance schizophrenia, Parkinson's disease, and Alzheimer's disease) from brain scans at early stages. In order to identify neurological disorders various methods use neuroimaging studies, mainly magnetic resonance imaging (MRI). Preprocessing MRI scans, data augmentation, deep feature extraction, and machine learning-based classification are all included in the suggested model. Image filtering and contrast augmentation techniques are applied to the acquired raw MRI images to improve their quality. Various methods are used to enhance the categorization accuracy of the current datasets. For automated deep feature extraction from input MRI pictures, we modified the Convolutional Neural Network's (CNN) layers. Deep feature extraction lowers the dimensionality and computing needs of an updated CNN by extracting complex properties from its deeper levels. The effectiveness of the suggested model for classifying neurological disorders is assessed using sophisticated machine learning classifiers. The suggested method is better at detecting neurological issues, according to experimental research on neurological illnesses and imaging modalities.