Artificial Intelligence and Reliability Metrics in Medical Image Analysis

2020 ◽  
pp. 172-185
Author(s):  
Yamini G. ◽  
Gopinath Ganapathy
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Quality Assurance ◽  
Human Body ◽  
Medical Image Analysis ◽  
Value Added ◽  
Musculoskeletal Injuries ◽  
Healthcare Applications ◽  
Digital Era ◽  
Medical Judgment

Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskeletal injuries, etc. A perspective skill could be obtained from the increased amount of data and a range of possible options could be obtained from the AI though they are difficult to detect with the human eye. Experts, who occupy as a spearhead in the field of medicine in the digital era, could gather the information of the AI into healthcare. But the field of radiology includes many considerations such as diagnostic communication, medical judgment, policymaking, quality assurance, considering patient desire and values, etc. Through AI, doctors could easily gain the multidisciplinary clinical platform with more efficiency and execute the value-added task.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

scholarly journals Medical Image Analysis Using Artificial Intelligence

2019 ◽  
Vol 30 (2) ◽  
pp. 49 ◽  
Author(s):  
Hyun Jin Yoon ◽  
Young Jin Jeong ◽  
Hyun Kang ◽  
Ji Eun Jeong ◽  
Do-Young Kang
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals What is new in computer vision and artificial intelligence in medical image analysis applications

2021 ◽  
Vol 11 (8) ◽  
pp. 3830-3853
Author(s):  
Jimena Olveres ◽  
Germán González ◽  
Fabian Torres ◽  
José Carlos Moreno-Tagle ◽  
Erik Carbajal-Degante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection

2021 ◽  
Vol 29 (3) ◽  
pp. 97-111
Author(s):  
Mohammed Baz ◽  
Hatem Zaini ◽  
Hala S. El-sayed ◽  
Matokah AbuAlNaja ◽  
Heba M. El-Hoseny ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

Artificial Intelligence and Big Data

2020 ◽  
Vol 237 (12) ◽  
pp. 1438-1441
Author(s):  
Soenke Langner ◽  
Ebba Beller ◽  
Felix Streckenbach
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Big Data ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Clinical Applications ◽  
Radiological Imaging ◽  
Learning Techniques

AbstractMedical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

scholarly journals A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 2040-2049
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Image ◽  
Vision Loss ◽  
Medical Image Analysis ◽  
Research Trend ◽  
Automated Systems ◽  
First Choice

Nowadays, artificial intelligence applications invade all of the fields including medical applications field. Deep learning, a subfield of artificial intelligence, in particular, Convolutional Neural Networks (CNN), have quickly become the first choice for processing and analyzing medical images due to its performance and effectiveness. Diabetic retinopathy is a vision loss disease that infects people with diabetes. This disease damages the blood vessels in the retina, hence, leads to blindness. Due to the sensitivity and complications involved in managing diabetics, designing and developing automated systems to detect and grade diabetic retinopathy is considered one of the recent research areas in the world of medical image applications. In this paper, the aspects of deep learning field related to diabetic retinopathy have been discussed. Various concepts in deep learning including traditional Artificial Neural Network (ANN) algorithm, ANN drawbacks in context of computer vision and image processing applications, and the best algorithm to overcome ANN drawbacks, CNN, have been elucidated along with the architecture. The paper also reviews an extensive summary of some works in the current research trend and future applications of the DL algorithms in medical image analysis for DR detection and grading. Furthermore, various research gabs related to building such automated systems for medical image analysis have been conferred – such as imbalance dataset which is considered one of the main performance issues that should be handled, the need of high performance computational resources to train deep and efficient models and others. This is quite beneficial for researchers working in the domain of medical image analysis to handle DR.

scholarly journals Editorial: Artificial Intelligence for Medical Image Analysis of Neuroimaging Data

2020 ◽  
Vol 14 ◽  
Author(s):  
Nianyin Zeng ◽  
Siyang Zuo ◽  
Guoyan Zheng ◽  
Yangming Ou ◽  
Tong Tong
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Neuroimaging Data
Sign in / Sign up

Export Citation Format

Share Document