Convolutional neural network for the classification of tomographic and X-ray images in the recognition system
Abstract
In this work, a tomographic and X-ray recognition system was proposed and constructed for the search and localization of pathologies. This system includes the following blocks: patient information input, medical image processing, output, classification of detected pathologies, database, report preparation. The article focuses on the peculiarities of the development of a convolutional neural network for the classification of tomographic and X-ray images in a recognition system designed for the search and localization of pathologies. As a result, a convolutional neural network was proposed for the classification of tomographic and X-ray images in the developed recognition system designed to search for and locate pathologies.
References
Anam М. An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation. Image, Graphics and Signal Processing. 2012. Vol. 10. Pp. 34-39.
Bansal S. Performance analysis of color based region split and merge and otsu’s thresholding techniques for brain tumor extraction. International Journal of Engineering Research and Applications. 2013. Vol. 3, issue 4. Pp. 1640-1643.
Hakeem A. A. A new approach to image segmentation for brain tumor detection using pillar k-means algorithm. International Journal of Advanced Research in Computer and Communication Engineering. 2013. Vol. 2, issue 3. Pp. 1429-1436.
Hough P. V.C. Method and means for recognizing complex patterns .U.S. Patent 3,069,654. -December 18, 1962.
Kharrat A. Detection of brain tumor in medical images. International Conference on Signals, Circuits and Systems. 2009. Pp. 1-6.
Logeswari T. An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. In-ternational Journal of Computer Theory and Engineering. 2010. Vol. 2, no. 4, pp. 1793-8201.
Marr D. Proceedings of the Royal Society of London. Series B, Biological Sciences. Vol. 207, no. 1167. Pp. 187-217.
Neeraj Sharma Automated medical image segmentation techniques. Journal of medical physics. 2010. No. 35. Pp. 3-14.
Prewitt J. M. S. Object enhancement and extraction, picture processing and psychopictorics. NY : Academic Pres, 1970. Pp. 75-149.
Rakesh M. Image segmentation and detection of tumor objects in MR brain images using fuzzy C-means (FCM) algorithm. Internation-al Journal of Engineering Research and Applications. 2012. No. 2, issue 3. Pp. 2088-2094.
Roberts L. G. Machine perception of three-dimensional solids. Optical and Electro-Optical Information Processing. MIT Pres, 1965. Pp. 159-197.
Robinson G. S. Edge detection by compass gradient masks. Computer Graphics and Image Processing. 1977. Vol. 6, no. 5. Pp. 492-502.
Sobel I. E. Camera models and machine perception, PHD dissertation. Stanford University, 1970. 303 р.
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