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Ari Suwondo
Nursing Department, Polytechnic Health Ministry of Semarang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Model Deteksi Parkinson Disease Berbasis Deep Learning Menggunakan Arsitektur VGG Lestari, Deva; Wibowo, Gatot Murti; Setiawan, Agung Nugroho; Suwondo, Ari; Susanto, Edy
Jurnal Imejing Diagnostik (JImeD) Vol. 12 No. 1 (2026): JANUARY 2026
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v12i1.14284

Abstract

Background: Parkinsons Disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, resulting in motor and non-motor impairments. Early diagnosis remains challenging due to subtle initial symptoms and the relatively low accuracy of clinical assessment during early disease stages. Magnetic Resonance Imaging (MRI) provides high-resolution anatomical visualization and has the potential to detect early morphological changes. Advances in deep learning offer opportunities for automated PD detection through MRI analysis. This study aims to develop a PD detection model using the VGG architecture and evaluate its performance on MRI images. Methods: This study employed a Research and Development (R&D) approach to construct a deep learning–based PD detection model. The dataset consisted of 2,000 brain MRI images (1,000 PD and 1,000 healthy controls) obtained from the open-source Kaggle platform. Preprocessing included image normalization and resizing to 256×256 pixels. The dataset was divided into 80% training data and 20% testing data. The model was developed using the VGG architecture and trained for 15 epochs with a batch size of 16. Model performance was evaluated using accuracy, precision, sensitivity, and specificity metrics. Results: The VGG model demonstrated excellent classification performance on the test dataset. Evaluation results showed an accuracy of 0.99, precision of 0.99, sensitivity of 0.98, and specificity of 0.99. The confusion matrix indicated that the model correctly classified 198 healthy control images and 196 PD images, with minimal misclassification. Visualization of MRI comparisons showed that the model was able to detect morphological changes in the substantia nigra, including loss of the normal curvature of the crus cerebri, as an early indicator of PD. Conclusions: The VGG-based PD detection model achieved very high performance in distinguishing PD from healthy controls using MRI images. These findings highlight the potential of deep learning as a tool for early PD detection. However, the use of Kaggle data as the primary dataset represents a limitation due to unverified acquisition standards and clinical quality. Therefore, further validation using multicenter clinical datasets is required to ensure the model’s generalizability to broader patient populations.
Co-Authors Achmad Zulfa Juniarto Agung Nugroho Setiawan Ahmaniyah, Ahmaniyah Anak Agung Gede Sugianthara Annisa, Rifka Apriyana Irjayanti Arwani Arwani Arwani Asmawati, Lilik Astuti, Rara Sri Endang Puji Baju Widjasena Bedjo Santoso Kadri Beniqna Maharani Besmaya Bina Kurniawan Budhi R, Kamilah Daniati, Nia Daniati Darmini Darmini Daru Lestantyo Dessy Triana Desyandri Desyandri Deviana, Meli Dhanio, Yeyen Wulandari Diyah Fatmasari Djamil, Masrifan Dwi Cahyanti, Dwi edy susanto Ekawati Ekawati Eko Naning Sofyanita Endah Kumala Dewi Erlina Krisanti Fachry Abda El Rahman Faridah, Salsabila Nur Ferry Ardhiansyah Fitriyaningsih, Erna Indah Freya Nazera Iskandar Gatot Murti Wibowo, Gatot Murti Gunasari, Lala Foresta Valentine Gurnita, Fauziah Winda Hadisaputro, Haryo Hamdan, Yusuf Lensa Hanif, Fastabiqul Haryono, Nathasia Elga Heni Hendriyani Heri Nugroho Hidajati, Kamilah Hidayah, Khoirotul Hiyana TD, Christin Husna, Dewi Asmaul Krisdiana Wijayanti Kurnianingsih Kurnianingsih, Kurnianingsih Ladyvia, Fiyola Laksana, Ni'matun Faizah Lestari, Deva Lucky Herawati M. Choiroel Anwar Maria Conchita Leyla Centis Martini Martini Mateus Sakundarno Adi, Mateus Sakundarno Melyana Nurul Widyawati Moh. Rivandi Dengo Muchlis Achsan Udji Sofro Mulya, Dimas Yuzril Nainggolan, Wiwiek Neni Susilaningsih Noor Pramono Nurharisah, Siti Nurjazuli Nurjazuli Pradesi, Regina Pranandya, Brian Ilham Pujiastuti, Rr. Sri Endang Putri, Feby Septania Putri, Rifa Fauziah Syaifia Rasipin Rasipin Rinandyawati, Kharissa Runjati Sarwendah Dewi Astuti, Sarwendah Dewi Siswi Jayanti Siti Rizki Amalia Sofro, Muchlis AU Sri Anureksi SRI RAHAYU Sri Rahayu Sudirman Sudirman Sudiyono Sudiyono Suharyo Hadisaputro Supriyana Supriyana, Supriyana Suroto Suroto Suryati Kumorowulan Syuhada, Ambar Dani Theresia Ristadeli Tinuk Istiarti Tri Utami, Annisa Rachmah Tri Wiyatini, Tri Ulfa Nurullita Wahyuningtyas, Miranda Gita Wahyuningtyas Walin Walin, Walin Waode Fitrah Sari Yuliani Setyaningsih Yuliatri, Risa Yulisnawati Yulisnawati Yulistina Yulistina Yuni Kusmiyati Yuniarti - Yunus, Fanny Thresia