Deep Learning-Based Hybrid Scenario for Classification of Periapical Lesions in Cone Beam Computed Tomography

Artificial intelligence has made revolutionary advances in medical imaging in recent years. Various algorithms and techniques are used in this scientific field to significantly improve the accuracy and speed of medical diagnosis and classification processes. In this direction, approaches have been improved, from the past to the present, to extract meaningful features from dental images and classify them accurately. Especially, high asymmetry in morphological balance, play a critical role in distinguishing pathological patterns from normal anatomy. In this study, we propose a scenario for the classification of periapical lesions, supported by a combination of improved image processing techniques and regularization strategies integrated into the VGG16 transfer learning architecture, as the experience and time criteria required for manual interpretation of lesion detection confirm the need for a computer-aided system. In this study, which was conducted on the UFPE public dataset, an improvement in the performance of the VGG16 transfer learning architecture was achieved, with 18 different regularization methods proposed. These values indicate optimized training within the parameters of avoiding overfitting, stability, generalizability, and high accuracy. This optimization has the potential to use as a decision support system for diagnosis and treatment processes in various subfields of the medical world.