Machine Learning Model to Detect Parkinson's Disease using MRI Data

The major goal is to create opportunities for deep learning algorithms to be used to understand better and diagnose Parkinson's disease. The loss of dopamine-producing neurons causes Parkinson's disease, a degenerative disorder. When the condition first manifests, patients have tremors, bradykinesia, poor posture, and balance among other mobility impairments that progressively get worse over time. Additionally, as the global ageing population grows exponentially, more people are developing Parkinson's disease, which places a significant financial strain on governments. Structural modifications in the brain of individuals suffering from Parkinson's disease, resulting from a deficiency in dopamine, can be visualized through the application of Magnetic Resonance Imaging (MRI). In this particular investigation, an attempt was made to classify MR images of both Parkinson's disease patients and unaffected individuals by utilizing a sophisticated deep learning neural network. The utilization of a convolutional neural network proves to be instrumental in augmenting the diagnostic capabilities concerning Parkinson's disease. The MR images are meticulously scrutinized and subjected to rigorous evaluation in order to derive accurate metrics pertaining to their classification. The primary objective of this investigation aims to establish an approach for accurately discerning and classifying ailments through the utilization of Magnetic Resonance Imaging (MRI). Deep learning exhibits remarkable proficiency in disease diagnosis and image interpretation. A burgeoning body of scholarly inquiry suggests that the identification and prognostication of Parkinson's disease may necessitate the application of deep learning methodologies. In terms of disease prediction, deep learning models wield immense potency. The best early detection accuracy deep learning algorithms are hence the subject of increased research. In this experiment, the illness stage will be determined by brain imaging. Also, to the study suggests an effective deep learning approach for early disease prediction utilizing MRI data. The data set came from Kaggle. In this article a deep learning method for diagnosing Parkinson's diseases is proposed that makes use of the CNN algorithm.