Medical Image Analysis (MedIA) using Deep Learning
Priyanka Israni1, Maulika S. Patel2

1Ms. Priyanka D. Israni, Computer Engineering,G H Patel College of Engineering & Technology, V. V. Nagar, Anand (Gujarat), India.

2Dr. Maulika S. Patel, Computer Engineering, G H Patel College of Engineering & Technology, V. V. Nagar, Anand (Gujarat), India.

Manuscript received on 25 April 2020 | Revised Manuscript received on 07 May 2020 | Manuscript Published on 22 May 2020 | PP: 21-25 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10070597S20 | DOI: 10.35940/ijitee.G1007.0597S20

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Medical Image analysis has gained momentum in the research since last ten years. Medical images of different modalities like X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound etc. are generated with an increase of 15% to 20% every year. Medical image analysis requires high processing power and huge memory for storing the medical images, processing them, extracting features for useful information and segment the interested area for analysis. Thus, here comes the role of deep learning which proves to be promising for medical image analysis. The major focus of the paper is on exploring the literature on the broad areas of medical image analysis like Image Classification, Tumor/lesion classification and detection, Organ/Sub-structure Segmentation, Image Registration and Image Construction/ Enhancement using deep learning. Paper also highlights the physiological and medical challenges to be taken care, while analyzing medical images. It also discusses the technical challenges of using deep learning for medical image analysis and its solutions.

Keywords: Convolutional Neural Network, MRI, CT-Scan, Transfer Learning.
Scope of the Article: Deep Learning