Convolutional neural network for bio-medical image segmentation with hardware acceleration |
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| Institution: | 1. NMAM Institute of Technology, Nitte, India;2. Sastra University, Tanjore, India;3. Dept. of ECE, NMAM Institute of Technology, Nitte, India;4. Jordan University of Science and Technology, Jordan;5. Universidad del Cauca, Colombia;1. Developmental Cognitive Neuroscience Laboratory, Department of Psychological and Brain Sciences, 1101 East Tenth Street, Indiana University Bloomington, Bloomington, IN 47405, United States;2. The Complexity Group, Department of Psychology, University of Cincinnati, PO Box 210376, Cincinnati, OH, United States;1. Neuroscientific System Theory Group, Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany;2. BMW Group E/E Architecture, Technologies, Parkring 19, 85748 Garching, Germany;3. Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON, Canada |
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| Abstract: | Application of artificial intelligence in Bio-Medical image processing is gaining more and more importance in the field of Medical Science. The bio medical images, has to go through several steps before the diagnosis of the disease. Firstly, the images has to be acquired and preprocessing has to be done and the data has to be stored in memory. It requires huge amount of memory and processing time. Among the preprocessing steps, edge detection is one of the major step. Edge detection filters the unwanted details in the image, and preserves the edges of the image, which describe the boundary of the image. In biomedical application, for the detection of the diseases, it is very essential to have the boundary detail of the acquired image of the organ under observation. Thus it is very essential to extract the edges of the images. Power is one of the main parameters that have to be considered while dealing with biomedical instruments. The biomedical signal processing instruments should be capable of operating at low power and also at high speed. In order to segregate the images into different levels or stage, we use convolutional neural networks for classification. By having a hardware architecture for image edge detection, the computational time for pre-processing of the image can be reduced, and the hardware can be a part of acquisition device itself. In this paper a low-power architecture for edge detection to detect the biomedical images are presented. The edge detection output are given to the system, which will diagnose the diseases using image classification using convolutional neural network. In this paper, Sobel and Prewitt, algorithms are used for edge detection using 180 nm technology. The edge detection algorithms are implemented using VLSI, and digital IC design of the architecture is presented. The algorithms for edge detection is co-simulated using MATLAB and Modelsim. The architecture is first simulated using CMOS logic and new method using domino logic is presented for low power consumption. |
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| Keywords: | Sobel Prewitt Domino RTL Convolutional neural network |
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