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@article{169605,
        author = {Dharmarao Bala Sree and Nalam Chaitanya Gopinath and Vallem Keerthna and Talluri Harsha Sri Sai Lakshmi and Vinoj J},
        title = {Parkinson’s Disease Detection Using Machine  Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1768-1773},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169605},
        abstract = {This study focuses on improving the diagnosis of Parkinson’s disease by using CNN (Convolutional Neural Net- work) technology to analyze brain MRI scans. Parkinson’s disease is a disorder that affects movement and is often hardto diagnose accurately due to similarities with other movement- related conditions. Our goal is to create a model that can tell the difference between people with Parkinson’s and those without it by detecting unique patterns in MRI scans. CNNs are powerful tools for recognizing detailed patterns in images, allowing the model to pick up on subtle changes in the brain that may not be obvious through standard examination. By training this model on a wide range of MRI images, we aim for it to make consistent, reliable diagnoses, helping doctors to reach conclusions faster and more accurately. This approach could reduce the time needed for diagnosis, cut down on errors, and ultimately help patients get the right treatment sooner.},
        keywords = {Parkinson’s Disease, Deep Learning, Convolutional Neural Network, Classification, Classifiers (SVM, Decision Tree, KNN, ANN).},
        month = {November},
        }