Meta-heuristic Techniques to Train Artificial Neural Networks for Medical Image Classification: A Review

(E-pub Ahead of Print)

Author(s): Priyanka Jindal*, Dharmender Kumar

Journal Name: Recent Advances in Computer Science and Communications
Formerly Recent Patents on Computer Science

Article ID: e140921185915
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Medical imaging has been utilized in various forms in clinical applications for better diagnostic and treatment of diseases. These imaging technologies help in recognizing body's ailing region with ease. In addition, it causes no pain to patient as the interior part of the body can be seen without opening too much of the body. Nowadays, various image processing techniques such as segmentation, registration, classification, restoration, contrast enhancement and many more exists to enhance image quality. Among all these techniques, classification plays an important role in computer-aided diagnosis for easy analysis and interpretation of these images. Image classification not only classifies diseases with high accuracy but also finds out which part of the body is infected by the disease. The usage of Neural networks classifier in medical imaging applications opened new doors or opportunities to researchers stirring them to excel in this domain. Moreover, accuracy in clinical practices and development of more sophisticated equipment is necessary in medical field for more accurate and quicker decisions. Therefore, keeping this in mind, researchers started focusing on adding intelligence by using meta-heuristic techniques to classification methods. This paper provides a brief survey on role of artificial neural networks in medical image classification, various types of meta-heuristic algorithms applied for optimization purpose, their hybridization. A comparative analysis showing the effect of applying these algorithms on some classification parameters such as accuracy, sensitivity, specificity is also provided. From the comparison, it can be observed that the usage of these methods significantly optimizes these parameters leading us to diagnosis and treatment of a number of diseases in their early stage.

Keywords: Medical Image classification, feature extraction (FE), Artificial Neural Networks (ANN), classification accuracy, Optimization, Hybridization, Meta-heuristic

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Article ID: e140921185915
(E-pub Ahead of Print)
DOI: 10.2174/2666255813999200915141534
Price: $95

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