IoT Based Intelligent Computer-Aided Diagnosis and Decision Making System for Health Care

2021 ◽  
Author(s):  
Channabasava Chola ◽  
Md Belal Bin Heyat ◽  
Faijan Akhtar ◽  
Omar Al Shorman ◽  
Bibal Benifa J V ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Decision Making System ◽  
Aided Diagnosis ◽  
Intelligent Computer

Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs

2008 ◽  
Vol 92 (3) ◽  
pp. 238-248 ◽  
Author(s):  
Hiroshi Fujita ◽  
Yoshikazu Uchiyama ◽  
Toshiaki Nakagawa ◽  
Daisuke Fukuoka ◽  
Yuji Hatanaka ◽  
...  
Keyword(s):  
Health Care ◽  
Computer Aided Diagnosis ◽  
Health Care Needs ◽  
Care Needs ◽  
Cad Systems ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm

2013 ◽  
Vol 3 (3) ◽  
Author(s):  
C. Lakshmi Devasena ◽  
M. Hemalatha
Keyword(s):  
Magnetic Resonance ◽  
Medical Diagnosis ◽  
Research Work ◽  
Detection Algorithm ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Feature Reduction ◽  
Computer Aided ◽  
Decision Making System ◽  
Aided Diagnosis

AbstractIn Medical Diagnosis, Magnetic Resonance Image (MRI) plays a momentous role. MRI is based on the physical and chemical principles of Nuclear Magnetic Resonance (NMR), a technique used to gain information about the nature of molecules. Retrieving a high quality MR Image for a medical diagnosis is critical. So denoising of Magnetic Resonance (MR) images and making them easy for human understanding form is a challenge. This research work presents an efficient Hybrid Abnormal Detection Algorithm (HADA) to detect the abnormalities in any part of the human body by MRIs. The proposed technique includes five stages: Noise Reduction, Smoothing, Feature Extraction, Feature Reduction and Classification. The proposed algorithm has been implemented and Classification accuracy of 98.80% has been achieved. The result shows that the proposed technique is robust and effective compared to other recent works. The system developed using the proposed algorithm will be a good computer aided diagnosis and decision making system in healthcare.

Problem Area Formation as an Element of Computer Aided Diagnosis: A Comparison of Two Strategies Within Quick Medical Reference (QMR)

1991 ◽  
Vol 30 (02) ◽  
pp. 90-95 ◽  
Author(s):  
L. Berman ◽  
R. A. Miller
Keyword(s):  
Decision Making ◽  
Case Analysis ◽  
Computer Aided Diagnosis ◽  
Problem Area ◽  
Alternative Approach ◽  
Computer Aided ◽  
Partitioning Algorithm ◽  
Aided Diagnosis ◽  
Computer Aided Decision

AbstractINTERNIST-I’s use of “partitioning” to group related diagnoses into problem areas (for competitive consideration and elimination during case analysis) is felt to be the source of many of its strengths as well as some of its weaknesses. QMR, INTERNIST-I’s successor program, embodies a homology function which can act as an alternative to the partitioner for problem area formation. This study undertakes a comparison of the problem areas generated by the INTERNIST-I partitioning algorithm, the QMR homology function, and expert clinicians; it finds the correlation to be poor. The authors then discuss another method of problem area formation which might better mimic a human clinician and provide an alternative approach in diagnostic computer-aided decision making.

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