Effects of the combined use of segmentation or detection models on the deep learning classification performance for cyst‐like lesions of the jaws on panoramic radiographs: Preliminary research

Abstract

AbstractAimTo evaluate the effects of the combined use of segmentation or detection models on the deep learning (DL) classification performance for cyst‐like lesions of the jaws on panoramic radiographs.MethodsThe panoramic radiographs of 536 patients with cyst‐like lesions of the jaws and 130 patients without cyst‐like lesions were used in this study. The radiographs were arbitrarily assigned to training, validation, and test datasets. The following three DL systems were created: System 1 directly classified cyst‐like lesions of the jaws on panoramic radiographs using a VGG‐16 convolution neural network (CNN), System 2 combined two CNNs to perform a preceding segmentation (U‐Net) and then the classification (VGG‐16), and System 3 combined two CNNs to perform a preceding detection (YOLO) and then the classification (VGG‐16). The classification performance of three systems was evaluated and compared with that of oral and maxillofacial radiologists.ResultsThe classification performance of System 2 was higher than the other DL systems, demonstrating the efficacy of the combined use of DL segmentation and classification models. System 3 followed it. The radiologists showed similar accuracy with System 2 and higher performance than Systems 1 and 3. The three DL systems and the radiologists all showed higher performance for dentigerous and radicular cysts than for ameloblastoma and odontogenic keratocysts, because of bias in the number of cases between categories even if data were collected at two institutions.ConclusionsThe performance of DL classification of cyst‐like lesions of the jaws was improved by the addition of a DL segmentation technique.