A Survey of Computer Aided Diagnosis (Cad) of Liver in Medical 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.

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Computer-aided Diagnosis and Medical Decision Support are not Synonymous

Methods of Information in Medicine ◽

10.1055/s-0038-1634612 ◽

1995 ◽

Vol 34 (04) ◽

pp. 369-370 ◽

Cited By ~ 2

Author(s):

F. T. de Dombal

Keyword(s):

Decision Support ◽

Computer Aided Diagnosis ◽

Medical Decision ◽

Medical Decision Support ◽

Computer Aided ◽

Aided Diagnosis

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Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

Methods of Information in Medicine ◽

10.1055/s-0038-1636061 ◽

1972 ◽

Vol 11 (01) ◽

pp. 32-37 ◽

Cited By ~ 10

Author(s):

F. T. DE DOMBAL ◽

J. C. HORROCKS ◽

J. R. STANILAND ◽

P. J. GUILLOU

Keyword(s):

Pattern Recognition ◽

Computer System ◽

Computer Aided Diagnosis ◽

Clinical Interview ◽

Computer Performance ◽

Pattern Size ◽

Computer Aided ◽

Computer Based ◽

Aided Diagnosis ◽

Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

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