Convolutional Neural Network for Automated Analyzing of Medical Images
A.Cibi1, R.Jemila Rose2

1Ms.A.Cibi*, Assistant Professor, Department of Computer Science and Engineeirng, Rajalakshmi Engineering College, Chennai.
2Dr.R. Jemila Rose, Associate Professor, St. Xavier’s Catholic College of Engineering, Nagercoil.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 687-691 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5629059720/2020©BEIESP | DOI: 10.35940/ijitee.G5629.059720
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Convolutional Neural Network CNN) is one of the deep learning algorithms. It is useful for finding patterns in images. Intelligent software automates understanding images and speech. Extracting distinct features, by their own induces intelligent to software for identifying objects, recognizing faces and diagnosing diseases from medical images. With the help of CNN, software on their own acquires the knowledge of patterns from raw data. These developments play a prominent role in medical imaging. Classification, Segmentation and diagnosing are the area where CNN marked its importance. About CNN there has been a large array of improvements achieved in the last few years. We provide a short overview of the role of CNN in medical image analysis. A shallow CNN model is proposed as an automatic diagnosing system. This work specifically concentrates on three key elements: (1) building blocks of convolutional neural networks (2) introduction of various CNN architecture; (3) Challenges in implementing CNN for analyzing medical images. 
Keywords:  Artificial Intelligence, Convolution Neural Network, Computer Vision, Medical Imaging.
Scope of the Article: Artificial Intelligence