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@article{195052,
        author = {Shivangi Pandey and Dr. Syed Tanzeem Ahmed},
        title = {Parkinson's Disease Medical Image Diagnosis Using Deep Learning and Optimize Features},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6568-6575},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195052},
        abstract = {Growing population increases the load of medical practioner and they need some assistance to improve the work quality. Man power has its limitation hence many of researchers provides various solutions to reduce the work load and increase quality. This paper has resolved the Parkinson disease detection by optimizing the input data and train a mathematical model. Proposed model has processed the input data by Group Search Optimization algorithm by clustering the image into features and noise region. Identified feature region image was used for the feature extraction. Extracted features were used for the training of the spiking neural network. As Parkinson have complex image structure hence spiking neural network give a clear and precise prediction class of the model. Experiment was done on real dataset have all set of classes and results shows that proposed Parkinson disease detection by Group Search optimization (PDDGSO) improve the accuracy by % as compared to comparing models.},
        keywords = {Genetic Algorithm, Image Processing, Machine Learning, Parkinson Detection.},
        month = {March},
        }