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This study proposes a method to identify Parkinson disease classifying patients as normal or
abnormal using the latest machine learning algorithms. The image is acquired, converted to gray scale,
preprocessed using Wiener filter. Canny edge detection method is used which involves image smoothing,
gradient operation, non maxima suppression, hysteresis thresholding and connectivity analysis. Then image
is segmented using fuzzy C-means. Features are extracted using GLCM technique and ANFIS classification is
used to classify patients as normal or abnormal. Experimental results proves that patients suffering from
neurological disease can be effectively detected using this method. A total of 167 spiral images were used out
of which 56 were normal patient and 111 were abnormal collected from various sources. A classification
accuracy of 99% is achieved.
