Arsitektur Hibrida CNN–LSTM Berbasis Retinex untuk Deteksi Lesi Periapikal pada Radiograf CBCT–Panoramik
DOI:
https://doi.org/10.58466/vc18dh25Kata Kunci:
Deep Learning, CNN-LSTM, Retinex, Lesi Periapikal, CBCT, Radiograf PanoramikAbstrak
Deteksi lesi periapikal berperan penting dalam diagnosis endodontik, namun interpretasi manual terhadap Cone-Beam Computed Tomography (CBCT) dan radiograf panoramik masih memerlukan waktu, bergantung pada pengalaman klinisi, serta rentan terhadap variasi diagnostik. Penelitian ini mengusulkan kerangka deep learning hibrida yang mengintegrasikan arsitektur Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM), dikombinasikan dengan peningkatan kualitas citra berbasis Retinex, untuk mendeteksi dan mengklasifikasikan lesi periapikal secara otomatis. Peningkatan Retinex diterapkan sebagai tahap praproses untuk menormalkan iluminasi dan meningkatkan kontras lesi. Model hibrida CNN-LSTM menangkap dependensi spasial dan kontekstual melalui pemrosesan patch sekuensial dari citra panoramik dan CBCT. Dengan menggunakan dataset 1.500 citra beranotasi yang dikumpulkan dari dataset radiografi klinis dan sumber terbuka, model yang diusulkan mencapai akurasi 97,8%, presisi 96,4%, recall 95,9%, dan F1-score 0,96, serta mengungguli model pembanding CNN dan U-Net secara signifikan. Hasil ini menunjukkan bahwa integrasi peningkatan citra dan deep learning hibrida dapat meningkatkan sensitivitas terhadap lesi kecil serta menurunkan false negative, sehingga menawarkan pendekatan yang layak secara klinis untuk diagnosis endodontik berbantuan AI.
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Hak Cipta (c) 2025 Safar Dwi Kurniawan, Tri Haryo Nugroho, David Bani Adam
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