PARKINSONS DISEASE DETECTION USING INCEPTION AND X-CEPTION WITH ATTENTION MECHANISM

Parkinson's disease is one of the global health challenges that requires early detection to slow the progression of symptoms. This study proposes an automatic detection system based on deep learning using the InceptionV3 and Xception architectures combined with a multi-head awareness mechanism. The dataset used consists of 72 handwritten spiral images, comprehensively distributed between the Healthy and Parkinson's categories. The process includes preprocessing in the form of normalization and image resizing, as well as model training using the Adam algorithm and the binary cross-entropy loss function. The results show that the model is able to classify both categories with high accuracy. The use of the attention mechanism provides a performance increase of 4.2% on InceptionV3 and 3.1% on Xception compared to the version without attention. In data testing, the InceptionV3 model with attention achieved 100% accuracy, 100% precision, 100% recall, and 100% F1-score. Meanwhile, the Xception model with attention achieved 88% accuracy, 90% precision, 88% recall, and 87% F1-score. The attention mechanism also helps the model in capturing important features such as vibration and irregularity of the spiral pattern. This research makes an important contribution to the development of an artificial intelligence-based automated early diagnosis system to detect Parkinson's disease more accurately and responsively.