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AU2021100684A4 DEPCADDX - A MATLAB App for Caries Detection and Diagnosis from Dental X-rays 未知
公开(公告)号：AU2021100684A4
公开(公告)日：2021-04-22
申请号：AU2021100684
申请日：2021-02-03
申请人： AJAY SHARMA AJAY SHARMA DR , C K JHA C K JHA PROF , SHARMA KARUNA MS , SAURABH MUKHERJEE SAURABH MUKHERJEE PROF
发明人： SHARMA KARUNA , SAURABH MUKHERJEE SAURABH MUKHERJEE , C K JHA C K JHA , AJAY SHARMA AJAY SHARMA
IPC分类号： G06T7/00 , G06N3/02 , G06N20/00 , G16H30/00 , G16H50/20
摘要： DEPCADDX - A MATLAB App for Caries Detection and Diagnosis from Dental X-rays In the aeon of deep learning, CNN outperform significant part in medical image analysis. So, Software Applications for caries detection can utilize significant features to detect and diagnose different types of caries. Now a day CNNs based application software are worth popular due to automatic relevant features extraction. CNNs can be trained from ground up for medical images but due to finite number of medical images transfer learning and data augmentations are used for training. Dental X-Rays can contain different types of caries in dentin, enamel, proximal and root surface of tooth anatomy. In this invention we have developed a MATLAB app for Caries detection and diagnosis like Dentin, Enamel and Pulp based on Hybrid CNN framework. This MATLAB app performs different steps to enhance contrast, to preprocess the dental X-ray. At the final step classify carious lesions into Dentin, Enamel and Pulp. It has been trained and examined on primary Dental X-rays available from a Dental Clinic. To develop this app we have performed experimentation with different CNNs like AlexNet, GoogleNet, ResNet50, VGG-19, Inception-v3, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet-v2, NASNetMobile and DenseNet-201 with different architectures and parameters using transfer learning approach due to limited number of Dental X-Rays and evaluated to propose best application for the detection of Caries like Dentin, Enamel and Pulp. DEPCADDX - A MATLAB App for Caries Detection and Diagnosis from Dental X-rays Input DentalX-Rays Divide Dataset into Training Dataset (70% and Testing Date t(30%) Resize DentalX-Rays accordingtotheInput Size of Selected Training Model Convert Gray Scale images to RGB images Perform Model Validation usingMoeVaiainadCsifctn Performance Matrices to Selected Figure 1B: Phases for development of Models for Caries detection and diagnosis. 21Page

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