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An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.
Kurmi, Ankit; Biswas, Shreya; Sen, Shibaprasad; Sinitca, Aleksandr; Kaplun, Dmitrii; Sarkar, Ram.
Afiliación
  • Kurmi A; Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani 741235, West Bengal, India.
  • Biswas S; Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, West Bengal, India.
  • Sen S; Department of Computer Science and Technology, University of Engineering and Management, Kolkata 700160, West Bengal, India.
  • Sinitca A; Research Centre for Digital Telecommunication Technologies, Saint Petersburg Electrotechnical University "LETI", 197022 St. Petersburg, Russia.
  • Kaplun D; Department of Automation and Control Processes, Saint Petersburg Electrotechnical University "LETI", 197022 St. Petersburg, Russia.
  • Sarkar R; Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, West Bengal, India.
Diagnostics (Basel) ; 12(5)2022 May 08.
Article en En | MEDLINE | ID: mdl-35626328
Parkinson's Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. Tremors in hands is usually the initial symptom, followed by rigidity, bradykinesia, postural instability, and impaired balance. Proper diagnosis and preventive treatment can help patients improve their quality of life. We have proposed an ensemble of Deep Learning (DL) models to predict Parkinson's using DaTscan images. Initially, we have used four DL models, namely, VGG16, ResNet50, Inception-V3, and Xception, to classify Parkinson's disease. In the next stage, we have applied a Fuzzy Fusion logic-based ensemble approach to enhance the overall result of the classification model. The proposed model is assessed on a publicly available database provided by the Parkinson's Progression Markers Initiative (PPMI). The achieved recognition accuracy, Precision, Sensitivity, Specificity, F1-score from the proposed model are 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively which are higher than the individual model. We have also developed a Graphical User Interface (GUI)-based software tool for public use that instantly detects all classes using Magnetic Resonance Imaging (MRI) with reasonable accuracy. The proposed method offers better performance compared to other state-of-the-art methods in detecting PD. The developed GUI-based software tool can play a significant role in detecting the disease in real-time.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Aspecto: Patient_preference Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Aspecto: Patient_preference Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza