<![CDATA[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.]]>
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