Open AccessMedical Image Analysis (MedIA) using Deep Learning
Author(s) -
M Priyanka,
Dippal Israni,
Gaurav Patel,
V. V. Nagar,
Maulika Patel,
Priyanka Israni
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g1007.0597s20
Subject(s) - deep learning , artificial intelligence , medical imaging , computer science , segmentation , focus (optics) , computer vision , image segmentation , image processing , modalities , image (mathematics) , social science , physics , sociology , optics
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.