Computer Aided Diagnosis System To Distinguish Adhd From Similar Behavioral Disorders

ADHD is one of the most prevalent psychiatric disorder of childhood, characterized by inattention and distractibility, with or without accompanying hyperactivity. The main aim of this research work is to develop a Computer Aided Diagnosis (CAD) technique with minimal steps that can differentiate the ADHD children from the other similar children behavioral disorders such as anxiety, depression and conduct disorder based on the Electroencephalogram (EEG) signal features and symptoms. The proposed technique is based on soft computing and bio inspired computing algorithms. Four non-linear features are extracted from the EEG such as Higuchi fractal dimension, Katz fractal dimension, Sevick fractal dimension and Lyapunov exponent and 14 symptoms which are most important in differentiation are extracted by experts in the field of psychiatry. Particle Swarm Optimization (PSO) tuned Back Propagation Neural Network (BPNN) and PSO tuned Radial Basis Function (RBF) employed as a classifier. By investigating these integrated features, we obtained good classification accuracy. Simulation results suggest that the proposed technique offer high potential in the diagnosis of ADHD and may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.

Download Full-text

Computer Aided Diagnosis of Early Esophageal Cancer From Endoscopic Image by Using Wavelet Transform and Fractal Dimension

The American Journal of Gastroenterology ◽

10.14309/00000434-201710001-00405 ◽

2017 ◽

Vol 112 ◽

pp. S216

Author(s):

Yasuhisa Sakata ◽

Natsuko Sakata ◽

Ryo Shimoda ◽

Koichi Miyahara ◽

Naoyuki Tominaga ◽

...

Keyword(s):

Esophageal Cancer ◽

Fractal Dimension ◽

Wavelet Transform ◽

Computer Aided Diagnosis ◽

Early Esophageal Cancer ◽

Endoscopic Image ◽

Computer Aided ◽

Aided Diagnosis

Download Full-text

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

Open Computer Science ◽

10.2478/s13537-013-0107-z ◽

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.

Download Full-text

Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.195-196.493 ◽

2012 ◽

Vol 195-196 ◽

pp. 493-497

Author(s):

Wu Qiu ◽

Feng Xiao ◽

Xin Yang ◽

Mao Lin Ye ◽

Yu Chi Ming ◽

...

Keyword(s):

Liver Cancer ◽

Back Propagation ◽

Normal Liver ◽

Computer Aided Diagnosis ◽

Support Vector ◽

Ultrasound Images ◽

Gray Level ◽

Fuzzy Enhancement ◽

Computer Aided ◽

Aided Diagnosis

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

Download Full-text

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

Download Full-text

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.

Download Full-text