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Medical Image Classification
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Author(s): Jesu Vedha Nayahi J. (Anna University Tirunelveli, India)and Gokulakrishnan K. (Anna University Tirunelveli, India)
Copyright: 2019
Pages: 22
Source title:
Medical Image Processing for Improved Clinical Diagnosis
Source Author(s)/Editor(s): A. Swarnambiga (Indian Institute of Technology Madras, India)
DOI: 10.4018/978-1-5225-5876-7.ch003
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Abstract
Diagnosis of diseases at the right stage with optimal accuracy is a significant requirement in the medical field. Apart from diagnosis from clinical symptoms, diagnosis based on scanned images of both internal and external organs is playing a vital role in understanding the severity of the disease. Classification is a field of study derived from artificial intelligence, and today it is widely used in medical image classification. These techniques are used to classify the different stages of a disease or different variant diseases possible in an organ from different types of input images such as magnetic resonance imaging (MRI), computed tomography (CT), x-ray, fundus images, iris images, etc. Various preprocessing techniques are used to select the relevant features from the input image to form the feature set. The classifiers are trained using the feature set to generate models. The generated models can be optimized to improve the performance. Various metrics such as accuracy, coverage, precision, recall, and FMeasure are used to measure the accuracy.
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