SCIT, Manipal University Jaipur, India.
Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India.
Comput Intell Neurosci. 2022 Jan 30;2022:4357088. doi: 10.1155/2022/4357088. eCollection 2022.
Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred choice of radiologists because of its high sensitivity and specificity. In the present work, deep learning algorithm based model, , has been proposed for the automation of bone invasion detection. performs the binary classification of CT scan images as the images with bone invasion and images without bone invasion. The proposed model has achieved an outstanding accuracy of 93.62%. The model is also compared with six Transfer Learning models like VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, ResNet-101 and BID-Net outperformed over the other models. As there exists no previous studies on bone invasion detection using Deep Learning models, so the results of the proposed model have been validated from the experts of practitioner radiologists, S.M.S. hospital, Jaipur, India.
检测口腔鳞状细胞癌（OSCC）患者肿瘤是否存在骨侵犯对其治疗计划和手术切除非常重要。对于骨侵犯检测，由于 CT 扫描成像具有高灵敏度和特异性，因此放射科医生首选 CT 扫描成像。在本工作中，提出了一种基于深度学习算法的模型，用于骨侵犯的自动检测。该模型对 CT 扫描图像进行二进制分类，分为有骨侵犯的图像和无骨侵犯的图像。所提出的 模型在骨侵犯检测方面取得了出色的准确率 93.62%。该模型还与六种迁移学习模型（如 VGG16、VGG19、ResNet-50、MobileNetV2、DenseNet-121、ResNet-101 和 BID-Net）进行了比较，优于其他模型。由于之前没有使用深度学习模型进行骨侵犯检测的研究，因此该模型的结果已经得到了印度斋浦尔 SMS 医院的放射科专家的验证。
