Medical Image Classification Based On Normalized Coding Network with Multiscale Perception
K. Arun Kumar1, P. Rajashekar Reddy2, Mahesh Kusuma3

1K. Arun Kumar, Asst. Professor CVR College of Engineering ,Hyderabad, area of Medical Image and Signal Processing, Embedded.
2P. Rajashekar Reddy, AsstProfessor CVR College of Engineering, Hyderabad, India.
3Mahesh Kusuma, Asst.Professor in Sreyas Institute of Engineering & Technology, Hyderabad, India.

Manuscript received on 23 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 2694-2697 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21430981119/2019©BEIESP | DOI: 10.35940/ijitee.K2143.0881119
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Abstract: Medical imaging classification is playing a vital role in identifying and diagnoses the diseases, which is very helpful to doctor. Conventional ways classify supported the form, color, and/or texture, most of tiny problematic areas haven’t shown in medical images, which meant less efficient classification and that has poor ability to identify disease. Advanced deep learning algorithms provide an efficient way to construct a finished model that can compute final classification labels with the raw pixels of medical images. These conventional algorithms are not sufficient for high resolution images due to small dataset size, advanced deep learning models suffer from very high computational costs and limitations in the channels and multilayers in the channels. To overcome these limitations, we proposed a new algorithm Normalized Coding Network with Multi-scale Perceptron (NCNMP), which combines high-level features and traditional features. The Architecture of the proposed model includes three stages. Training, retrieve, fuse. We examined the proposed algorithm on medical image dataset NIH2626. We got an overall image classification accuracy of 91.35, which are greater than the present methods.
Keywords: Normalized Coding Network with Multilayer Perception, Distant Domain Transfer Learning, Deep Learning
Scope of the Article: Deep Learning