A Study on Supervised Learning in Medical Image Gradingusing IoT
A.Vidhyalakshmi1, C. Priya2

1A. Vidhyalakshmi, Research Scholar, School of Computing Science, & Advanced Studies VISTAS, Vels Institute of Science, Technology, Chennai (Tamil Nadu), India.
2C. Priya, Associate Professor & Research Supervisor, School of Computing Science, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 13 February 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 28 April 2019 | PP: 274-279 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10620275C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Computerized medical imaging techniques is a process of creating visual interior representationof our body part for examining clinical analysis and disrupting the visual functionsof some organs or tissues. The medical images analyze the types of diseases present in the organs. The intensity often grades the severity and measures the risk score of diseased image using the Medical Image grading techniques. The Machine learning algorithms constructs a mathematical pattern of sample data known as training data, such that to make predictions or decisions without being explicitly programmed to perform the task. TheSupervised learning inmachine learning is a task of investigating a function that maps input to an output which is based on input-output pairs. This paper presents the review about the medical images and medical image grading techniques. The survey for the various result analyses of medical images using the image processing techniques and the overview of the diseases present in the tongue, hand and lungs images are discussed. In this paper we talk about the introduction of medical image analysis, medical image grading techniques applied for the detection of lung cancer in twoways such as, metastasis and the Lingual acrometastatic disease in the organ of tongue and hand. The proposed model includes the analysis of metastasis and the Lingual acrometastatic analysis using the classification techniques. The implantation of the proposed paper will be completedthrough the MATLAB software using the digital image processing techniques and the simulation results will be stored in the Internet of Things (IoT) server for future verification process. The prediction of lungs cancer will be compared by the results of the tongue and hand diseases will be stored present in the images.
Keywords: Classification, Supervised Learning, Detection, Segmentation, Medical Image Analysis.
Scope of the Article: IoT