Disease discovery is an important task in medicinal science. Computer has presented early disease discovery in medical science through image processing techniques. This paper is devoted to the exploration of brain images for early detection of Parkinson's disease. All brain images are analyzed to extract Gabor 2D features. It is also shown that the models created on Gabor features outperform the ones created without Gabor features. The experimental results have been conducted on a dataset of brain images of the individuals that are diagnosed to be real cases of Parkinson's diseases by expert clinicians. Three special experimentations are conducted in the paper.