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A new technique for detecting dental diseases by using high speed neuro-computers

Hazem M. El‐Bakry,Nikos E. Mastorakis-2008-09-11
6

TL;DRAbstract

In this paper, a new fast algorithm for dental diseases detection is presented. Such algorithm relies on performing cross correlation in the frequency domain between input image and the input weights of fast neural networks (FNNs). It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations. One of the limitations of Direct Digital Radiography (DDR) is noise. Some recent publications have indicated that Digital Subtraction Radiography (DSR) might significantly aid in the clinical diagnosis of dental diseases, once various clinical logistic problems limiting its widespread use have been over come. Noise in digital radiography may result from sources other than variation in projection geometry during exposure. Structure noise consists of all anatomic features other than those of diagnostic interest. Limi

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In this paper, a new fast algorithm for dental diseases detection is presented. Such algorithm relies on performing cross correlation in the frequency domain between input image and the input weights of fast neural networks (FNNs). It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations. One of the limitations of Direct Digital Radiography (DDR) is noise. Some recent publications have indicated that Digital Subtraction Radiography (DSR) might significantly aid in the clinical diagnosis of dental diseases, once various clinical logistic problems limiting its widespread use have been over come. Noise in digital radiography may result from sources other than variation in projection geometry during exposure. Structure noise consists of all anatomic features other than those of diagnostic interest. Limi

Keywords

RadiographyNoise (video)Computer scienceArtificial intelligenceSubtractionDigital radiographyComputationComputer vision

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