Automated Diagnosis and Severity Measurement of Cyst in Dental X-ray Images using Neural Network(<Special Issue>Biosensors: Data Acquisition, Processing and Control)
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概要
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One important aspect of dentofacial image processing is the utilization of comprehensive oral and maxillofacial imaging modalities that aid in the assessment, treatment planning and evaluation of oral diseases. Also, it plays a vital role in identifying the pathological conditions, and it improves the practice management of the doctors for diagnosis. In this paper Radial Basis Function (RBF) Neural Network for automated diagnosis of cysts in dental X-ray Images is proposed. The severity of the cysts is then evaluated based on gray level properties, circularity and area. The results obtained provide the complete information about severity of the cysts by increasing the diagnostic ease of the dental surgeon.
著者
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Raju S.
Department Of Electronics And Communication Engineering Thiagarajar College Of Engineering
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KUMAR V.
Department of Clinical Pharmacology and Therapeutics, Nizam's Institute of Medical Sciences
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Kumar V.
Department Of Clinical Pharmacology And Therapeutics Nizam's Institute Of Medical Sciences
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Banumathi A.
Department of Electronics and Communication Engineering, Thiagarajar College of Engineering
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Mallika J.
Department of Electronics and Communication Engineering, Thiagarajar College of Engineering
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Mallika J.
Department Of Electronics And Communication Engineering Thiagarajar College Of Engineering
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Banumathi A.
Department Of Electronics And Communication Engineering Thiagarajar College Of Engineering
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Kumar V.
Department Of Electronics And Communication Engineering Thiagarajar College Of Engineering
関連論文
- Automated Diagnosis and Severity Measurement of Cyst in Dental X-ray Images using Neural Network(Biosensors: Data Acquisition, Processing and Control)
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