Machine Learning Techniques in Lung Nodule Diagnosis of Medical Health Care Data
Popuri Ramesh Babu1,  Inampudi Ramesh Babu2

1Mr. Popuri ramesh babu, Research Scholar, Dept. of CSE, Acharya Nagarjuna University, Guntur, A.P.
2Dr. Inampudi ramesh babu, Professor, Dept. of CSE, Acharya Nagarjuna University, Guntur, A.P.

Manuscript received on 27 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 1458-1463 | Volume-8 Issue-11, September 2019. | Retrieval Number: J97910881019/2019©BEIESP | DOI: 10.35940/ijitee.J9791.0981119
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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: Machine learning is an essential domain of research and is efficiently used in various fields like finance, clinical research, knowledge, healthcare, etc. In healthcare, Machine learning is becoming more and more popular, if not habitually required. Machine learning techniques play a vital role in uncovering new trends in the healthcare organization in especially lung nodule diagnosis. Which is also for all the parties connected with this field. Further, the focus of this review on Research is to receive the state of art study work using machine learning approaches in the area of lung nodule diagnosis. In this approach, we further include some public analysis carried out in Medical field, producing different CAD systems in the medical domain in the area of lung nodule diagnosis using pattern recognition and image processing approaches. We focus on the work which is carried out with recent classifiers used on lung nodule diagnosis, and also, we list some research on machine learning techniques. The main reason for this survey paper is to present a survey on in advance used system gaining knowledge of techniques within the place of lung nodule analysis and provide legitimate results analysis.
Keywords: Medical imaging, Lung nodule diagnosis, Medical Data mining. Patter Recognition, Machine learning.
Scope of the Article: