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Classification and detection of dental images using meta-learning

World Journal of Clinical Cases 2024年第32期12卷 6559-6562页

作者： Pradeep Kumar Yadalam Raghavendra Vamsi Anegundi Mario Alberto Alarcón-Sánchez Artak Heboyan Department of Periodontics Saveetha Dental College and HospitalSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai 600077Tamil NaduIndia South Pacific dental Institute Chilpancingo de los Bravo 39022GuerreroMexico Department of Prosthodontics Faculty of StomatologyYerevan State Medical University after Mkhitar HeratsiYerevan 0025Armenia

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal *** of just memorizing a task,this is accomplished through teaching a model how to *** for meta-learning are typically trained on a collection of training problems,each of which has a limited number of labelled *** Xray classification tasks,including the detection of pneumonia,coronavirus disease 2019,and other disorders,have demonstrated the effectiveness of ***-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning *** to the high cost and lengthy collection process associated with dental imaging datasets,this is significant for dental X-ray classification *** ability to train models that are more resistant to fresh input is another benefit of meta-learning.

关键词： Artificial intelligence Meta-learning dental diagnosis Image segmentation Medical image interpretation dental radiography

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Expert System for the diagnosis and Prognosis of Common dental Diseases Using Bayes Network

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Journal of Biomedical Science and Engineering 2021年第11期14卷 361-370页

作者： Grace Tam-Nurseman Philip Achimugu Oluwatolani Achimugu Hilary Kelechi Anabi Sseggujja Husssein Department of Computer Science Lead City University Ibadan Nigeria Department of Computer Science Air Force Institute of Technology Kaduna Nigeria Department of Information and Communication Engineering Air Force Institute of Technology Kaduna Nigeria Department of Computer Science Islamic University in Uganda Mbale Uganda

Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.

关键词： Expert System dental diagnosis dental Diseases and Human Expert

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Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

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Computers, Materials & Continua 2021年第2期66卷 1613-1629页

作者： G.Arun Sampaul Thomas Y.Harold Robinson E.Golden Julie Vimal Shanmuganathan Seungmin Rho Yunyoung Nam CSE Department J.B.Institute of Engineering and TechnologyHyderabad500075India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Anna University Regional CampusTirunelveli627007India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Software Sejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision *** of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked *** this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate *** CNN model is trained by different images of eyes that have retinopathy and those which do not have *** fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent *** feature loss factor increases the label value to identify the patterns with the kernel-based *** performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and *** results show that the proposed CNN performs better.

关键词： Convolutional neural networks dental diagnosis image recognition diabetic retinopathy detection

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