Klasifikasi Penyakit Alzheimer Dari Scan Mri Otak Menggunakan Convnext
DOI:
https://doi.org/10.25126/jtiik.1168117Abstrak
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.
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