Development of Deep Learning Algorithm using Convolutional Neural Network for Medical Imaging
Kunal S Khadke

Kunal S Khadke, Assistant Professor, Department of Computer Engineering, PES Modern College of Engineering, Pune, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 139-142 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4912029320/2020©BEIESP | DOI: 10.35940/ijeat.C4912.029320
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Abstract: Medical imaging is the procedure and approach of formulating graphic models of the peculiarity of a body system for medical investigation and treatment, and also graphical illustration of the function of several internal organs or structures. To identify the affected tissues of the brain in a case of brain tumors, it is important to get high precision and accuracy to locate exact pixels. Manual analysis may be erroneous and so it is important to use deep learning image segmentation technique. Segmentation of graphic is the technique of dividing a graphic in to several group of pixels. The earlier objective of the segmentation is actually to produce details much easier and enhance the manifestation of clinical images into significant content. Segmentation is a complicated activity due to the excessive variability in the graphics. The computational intelligence is modern way for application automation. Existing studies shows need of deep learning research for fast and accurate medical imaging solutions. Hence, this paper presents the CNN framework (for an analysis of brain tumors) as a base for further research methodology development. The paper also provides a pilot research analysis that can further be used to develop improved precision and visibility.
Keywords: deep learning, CNN, brain tumor, U-cnet, feature extraction, segmentation, augmentation, machine learning.