Handbook of image processing operators
Handbook of image processing operators
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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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. Limitations of plain radiographs in detecting early, small bone lesions are also due to the presence of structure noise. This research work has been under - taken in an attempt to minimize structure noise in digital dental radiography by using digital subtraction radiography. By minimizing the structure noise, the validity of the digitized image in detecting diseases is enhanced.