Mendeley
TY  _  JOUR
ID  -  2022053137936
TI  -  Convolutional Neural Networks for Medical Image Segmentation and Classification: A Review
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  S JENIFER 
AU  -   
AD  -  
AD  -  School of Computing
Y1  -  2023
PY  -  2023
VL  -  44
IS  -  
SP  -  347
EP  -  358
KW  -  Convolutional Neural Networks
KW  -   Deep learning
KW  -   Generative Adversarial Network
KW  -   Medical Image Analysis
KW  -   Transfer learning.
DO  -  
N2  -  Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep Convolutional Neural Networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the works exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pre-trained models and General Adversarial Networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on covid-19 detection and child bone age prediction.


UR  - rimag.ir/en/Article/37936
L1  - rimag.ir/en/Article/Download/37936
ER  -  
