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.