Open access Jurnal SINTA 2
Klasifikasi Penyakit Alzheimer Dari Scan Mri Otak Menggunakan Convnext
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 11 No. 6 (2024) · pp. 1223–1232
- ISSN
- 2528-6579, 2355-7699
- Published
- 2024-12-10
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Bahasa Indonesia
Penyakit Alzheimer adalah gangguan neurodegeneratif yang menyebabkan penurunan fungsi kognitif yang signifikan. Penanganan penyakit ini dapat dilakukan melalui deteksi dini untuk meningkatkan kualitas kehidupan pasien melalui perawatan medis yang efisien dan tepat waktu. Teknologi machine learning dan neural network dapat mendukung deteksi dini melalui penggunaan model ConvNeXt yang telah dilatih dengan metode transfer learning menggunakan bobot awal dari ImageNet, dan di-fine-tune untuk mengklasifikasikan empat tingkat keparahan Alzheimer berdasarkan hasil pemindaian MRI otak, yaitu Mild Demented, Moderate Demented, Non Demented, dan Very Mild Demented. Penelitian ini akan menghasilkan model h5 dengan akurasi yang lebih baik daripada model lain sehingga dapat di-deploy pada aplikasi atau website untuk membantu deteksi dini klasifikasi tingkat keparahan Alzheimer. Abstract A Alzheimer's disease is a neurodegenerative disorder that causes significant cognitive decline. Early detection is crucial for managing this disease to improve patients' quality of life through efficient and timely medical care. Machine learning and neural network technology can support early detection through the use of the ConvNeXt model, which has been trained using transfer learning with initial weights from ImageNet and fine-tuned to classify four stages of Alzheimer's severity based on brain MRI scans: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. This research will produce an h5 model with better accuracy than other models, enabling it to be deployed in applications or websites to assist in the early detection and classification of Alzheimer's severity.
English
Penyakit Alzheimer adalah gangguan neurodegeneratif yang menyebabkan penurunan fungsi kognitif yang signifikan. Penanganan penyakit ini dapat dilakukan melalui deteksi dini untuk meningkatkan kualitas kehidupan pasien melalui perawatan medis yang efisien dan tepat waktu. Teknologi machine learning dan neural network dapat mendukung deteksi dini melalui penggunaan model ConvNeXt yang telah dilatih dengan metode transfer learning menggunakan bobot awal dari ImageNet, dan di-fine-tune untuk mengklasifikasikan empat tingkat keparahan Alzheimer berdasarkan hasil pemindaian MRI otak, yaitu Mild Demented, Moderate Demented, Non Demented, dan Very Mild Demented. Penelitian ini akan menghasilkan model h5 dengan akurasi yang lebih baik daripada model lain sehingga dapat di-deploy pada aplikasi atau website untuk membantu deteksi dini klasifikasi tingkat keparahan Alzheimer. Abstract A Alzheimer's disease is a neurodegenerative disorder that causes significant cognitive decline. Early detection is crucial for managing this disease to improve patients' quality of life through efficient and timely medical care. Machine learning and neural network technology can support early detection through the use of the ConvNeXt model, which has been trained using transfer learning with initial weights from ImageNet and fine-tuned to classify four stages of Alzheimer's severity based on brain MRI scans: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. This research will produce an h5 model with better accuracy than other models, enabling it to be deployed in applications or websites to assist in the early detection and classification of Alzheimer's severity.
Collaboration network of the authors
40 authors · 110 co-authorship links · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 2 |
| Achmad Ridok | University of Brawijaya | 2 |
| Yuita Arum Sari | University of Brawijaya | 1 |
| Fadhil Yusuf Rahadika | University of Brawijaya | 1 |
| Agus Wahyu Widodo | University of Brawijaya | 1 |
| Bayu Rahayudi | University of Brawijaya | 1 |
| M. Sofyan Irwanto | University of Brawijaya | 1 |
| Candra Dewi | University of Brawijaya | 1 |
| Nurfatima Selle | University of Brawijaya | 1 |
| Zainal Arifien | University of Brawijaya | 1 |
| Fitra Abdurrahman Bachtiar | Affiliation unknown | 1 |
| Alvin Tarisa Akbar | University of Brawijaya | 1 |
| Michael Stephen Lui | University of Brawijaya | 1 |
| Daniel Geoffrey Manurung | University of Brawijaya | 1 |
| Mohammad Ryan Pinasthika | University of Brawijaya | 1 |
| Muhammad Azka Obila Vasya | University of Brawijaya | 1 |
| Rania Aprilia Dwi Setya Putri | University of Brawijaya | 1 |
| Agustinus Parasian Tampubolon | University of Brawijaya | 1 |
| Rakan Fadhil Prayata | University of Brawijaya | 1 |
| Septia Khoirin Nisa | University of Brawijaya | 1 |
| Assyfa Rasida Hanum | University of Brawijaya | 1 |
| Ivykaeyla Adriana Zetha | University of Brawijaya | 1 |
| Salwa Cahyani Putri | University of Brawijaya | 1 |
| Rafifah Ayud Wulandari | University of Brawijaya | 1 |
| Sherla Puspa Andina | University of Brawijaya | 1 |
| Julia Nur Fajrina | University of Brawijaya | 1 |
| Mohammad Alfiano Rizky Mahardika | University of Brawijaya | 1 |
| Sulthan Abiyyu Hakim | University of Brawijaya | 1 |
| Muhammad Ubaidillah | University of Brawijaya | 1 |
| Aditya Ramadhan | University of Brawijaya | 1 |
| Rahmada Zulvia Azzahra Hawari | University of Brawijaya | 1 |
| Audhinata Bebytama Rizky | University of Brawijaya | 1 |
| Lailil Muflikhah | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.