This study investigates the feasibility of using learning models as alternative methods for brain analysis, particularly in evaluating damage from neurodegenerative diseases. The main objective is to compare their effectiveness and assess the impact of results on future feature utilization. A comparative analysis highlights the strengths and weaknesses of deep learning versus traditional methods. Additionally, a practical experiment is conducted using both approaches to test the software in a real-world setting. This research aims to advance brain analysis techniques and provide insights for accurate assessment of neurodegenerative disease-induced damage.