A Methodical Investigation of Parkinson s Disease Severity Assessment through analysis of MRI images using Enhanced Deep learning approach

Abstract

Worldwide, Parkinson s disease (PD) is a common condition resulting from a slow and newlinesteady degeneration of the brain. Even though there isn t a recognized treatment for this newlinekind of neurological condition right now, early detection and intervention can help patient s lead better lives. An individual s medical record includes medical images utilized for monitoring, treating, and managing their ailment. However, disease identification in computer-based diagnostics is difficult because of the high rate of error marking and energy consumption required. To overcome this problem, the first work provides SDA,feature selection method for PD categorization. Preprocessing first increases performance by reducing noise and retaining crucial information. Additionally, data enhancement increases the dataset s size and resolves the overfitting issue. After preprocessing is complete, the image arrives for selected features. From the preprocessed images,the PIO determined that the optimal characteristics for analysis were multi-texture and grey-level features. For feature selection, the IPIO approach is employed. Compared to previous strategies, the IPIO algorithm-based decision-making method chooses better features. The last step is to categorize the images as either Parkinson-affected or healthy using a stacked denoising autoencoder. The system s effectiveness is attested to by extensive experimental investigations on the conventional database and comparative assessments conducted with cutting-edge techniques. newlineFurthermore, the goal of present Parkinson s disease treatment approaches is to slow the newlineprogression of the disease and reduce symptoms, but a permanent cure is still unattainable. newlineThis research aims to outline potential approaches to early illness identification. newlineTherefore, an optimized MobileNetV3 is proposed for categories of Parkinson s illness that were identified by analyzing MRI images. The MobileNet V3 approach was optimized through the utilization of the IDMO.We presented a unique PCFAN that extracts character