Arsitektur Hibrida CNN–LSTM Berbasis Retinex untuk Deteksi Lesi Periapikal pada Radiograf CBCT–Panoramik - Safar Dwi Kurniawan; Tri Haryo Nugroho; David Bani Adam (Pengelola Jurnal Politeknik Negeri Ketapang, 2025)
Periapical lesion detection plays a crucial role in endodontic diagnosis; however, manual interpretation of Cone-Beam Computed Tomography (CBCT) and panoramic radiographs remains time-consuming, highly dependent on the clinician's expertise, and susceptible to diagnostic variability. This study proposes a hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, combined with Retinex-based image enhancement, for the automatic detection and classification of periapical lesions. Retinex enhancement is employed as a preprocessing step to normalize illumination and improve lesion contrast. The hybrid CNN-LSTM model captures both spatial and contextual dependencies through sequential patch-based processing of panoramic and CBCT images. Using a dataset of 1,500 annotated images collected from clinical radiographic datasets and publicly available sources, the proposed model achieved an accuracy of 97.8%, precision of 96.4%, recall of 95.9%, and an F1-score of 0.96, significantly outperforming conventional CNN and U-Net models. These findings demonstrate that the integration of image enhancement and hybrid deep learning improves sensitivity to small lesions while reducing false-negative detections, offering a clinically viable AI-assisted approach for endodontic diagnosis.