Machine Learning and Deep Learning Models for Diagnosis of Parkinson’s Disease: A Performance Analysis

Mounika, P.; Rao, S. Govinda

doi:10.1109/i-smac52330.2021.9640632

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…More advanced approaches in PD diagnosis have leveraged Deep Learning, a more complex iteration of ML, which has shown proficiency in handling high-dimensional data and capturing intricate patterns that simpler ML models may overlook (Mounika and Rao 2021). Convolutional Neural Networks, a class of deep neural networks, have been particularly effective in image-based diagnosis of PD, especially in interpreting DaTscan SPECT images to identify dopaminergic deficits (Khachnaoui et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Preemptive Diagnosis of Parkinson's Disease through DaT Scans using InceptionV3-based Convolutional Neural Networks

Appineni,

Gupta

2023

Preprint

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In this study, we introduce a neural-learning based approach for the early detection of Parkinson Disease using DaT SPECT imaging, a promising technique in neurodegenerative disease diagnosis. Leveraging the advanced capabilities of deep learning, we developed a convolutional neural network model based on the Inception V3 architecture, which has been enhanced by modulating custom layers to fine-tune the model specifically for the analysis of DaT SPECT images. The imaging dataset was obtained through the Parkinson's Progression Markers Initiative (PPMI), and the model was trained to classify whether a patient had Parkinson Disease through a binary classification of their DaT SPECT Scan using the fine-tuned InceptionV3 model. This specialized adaptation allows for more accurate and nuanced interpretation of the scans, a crucial factor in early and reliable diagnosis of Parkinsonian syndromes. Our model underwent rigorous hyperparameter tuning, focusing on learning rates, the number of neurons in the dense layer, and dropout rates, to ensure optimal performance. Remarkably, the proposed model demonstrated a significant improvement in accuracy, achieving 74.65%, compared to a standard Sequential Model, which only reached an accuracy of 51.41%. Diagnosis of Parkinson Disease through DaT SPECT Scans is a promising approach not only as a more effective diagnostic tool but also as a beacon for future research in the application of deep learning to medical imaging for neurodegenerative diseases. By building layers on the InceptionV3 Architecture, we achieve a higher accuracy on DaT SPECT Scan diagnosis and pave the way for automated diagnosis of patients with Parkinson Disease, allowing for easier medical accessibility and paving the way for advancements in early diagnosis and personalized medicine strategies for Parkinson's disease and related disorders.

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Section: Literature Reviewmentioning

confidence: 99%

Preemptive Diagnosis of Parkinson's Disease through DaT Scans using InceptionV3-based Convolutional Neural Networks

Appineni,

Gupta

2023

Preprint

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“…Mounika &Rao(2021) [26], study tests the proficiency of profound learning and machine learning approaches in arrange to distinguish the foremost exact methodology for detecting Parkinson's infection at an early organize. In arrange to degree the normal execution most precisely the creators compared profound learning and machine learning strategies.…”

Section: Internationalmentioning

confidence: 99%

Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature

Devi

2022

IJCSBE

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Purpose: A continuous Nervous system illness that influences development is known as neurodegenerative sickness. Side effects show up continuously, and may start with a scarcely distinguishable quake in just a single hand. Quakes are normal, but they are frequently joined by firmness or eased back versatility. The emphasis is on Parkinson's infection specifically (PD). The signs and results of Parkinson's contamination shift starting with one individual then onto the next. Early reprimand markers could be subtle and go unnoticed. Regardless, when aftereffects start to influence the different sides of your body, secondary effects normally jump on single side of your body and decay on that side. Parkinson's illness is brought about by the demise of nerve cells in the substantia nigra, a portion of the cerebrum. The exactness of a few fake brain network approaches, for example, Convolutional Neural Network, Recurrent Neural Network, Long-Short term Memory network have been concentrated on to analyze Parkinson's illnesses. This record inspects a synopsis of a portion of the examination and concentrates on that have been directed in the field of clinical diagnostics. In light of the audit, research holes are featured, as well as examination needs for future review. Approach: A thorough study on the algorithms used in analysis of handwritten and vocal to distinguish and anticipate Parkinson's illness. Findings: The review showed that the majority of the AI and deep learning strategy can order neurodegenerative illness in view of vocal, transcribed and walk investigation in light of the clinical datasets. The new crossover philosophy proposed will be more exact as the model will actually want to foresee and distinguish neurodegenerative sickness in view of eye development. Originality: The sort of information expected for forecast and discovery framework are considered and the design and portrayal outline of a proposed model are incorporated. Paper Type: Literature Review.

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“…Maunika and Rao [2] proposed the use of K-Nearest Neighbour (KNN), State-Of-The-Art Supervised ML Algorithm with the value of k=5 to achieve an accuracy of 97.43%. They have also implemented Deep Learning Algorithms to obtain substantial results in predicting the severity of Parkinson's Disease.…”

Section: Related Workmentioning

confidence: 99%

“…In spite of using a relatively small dataset, the implemented Machine Learning Models [10] came up with impressive results. With inundation of more data, our implemented Models are likely to improve even further.…”

Section: ) Statusmentioning

confidence: 99%

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

Sen¹,

GHOSH²

2022

Preprint

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Machine Learning and Deep Learning Models for Diagnosis of Parkinson’s Disease: A Performance Analysis

Cited by 12 publications

References 8 publications

Preemptive Diagnosis of Parkinson's Disease through DaT Scans using InceptionV3-based Convolutional Neural Networks

Preemptive Diagnosis of Parkinson's Disease through DaT Scans using InceptionV3-based Convolutional Neural Networks

Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

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