Artificial Neural Networks in Medical Images for Diagnosis Heart Valve Diseases
Neural networks are currently a hot research area in medicine. Medical image recognition algorithms have been widely applied to help with the diagnosis of various diseases more accurately. This paper presents an image processing-based artificial neural network for the diagnosis of heart valve diseases. The goal of the paper is to implement image processing techniques by extracting texture features from medical echocardiography images, combining intensity histogram features and Gray Level Co-occurrence Matrix (GLCM) features, then developing an artificial neural network for automatic classification based on back-propagation algorithm to classify heart valve diseases more accurately. The proposed method performance was evaluated in terms of precision, recall and accuracy. The experimental results confirm the efficiency of the proposed method that provides good classification efficiency.
Keywords: Computer-Aided Diagnosis (CAD), Neural networks, Texture Features, Intensity Histogram Features, GLCM Features, Back-Propagation Classifier, Heart Valve Diseases.
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ABOUT THE AUTHORS
Atta Elalfi
Mohamed Eisa
Hosnia Ahmed
Atta Elalfi
Mohamed Eisa
Hosnia Ahmed
