Abstract
A Convolutional Neural Network (CNN) is an effective Artificial Intelligence (AI) technique for the automation of image analysis. However, to achieve a high level of accuracy, a CNN usually requires a large amount of data and a long training time. The current study addresses the above problem by proposing a novel AI technique. The latter can detect and classify abnormalities in images using a small amount of available data and a short training time. The proposed technique, Artificial Intelligence Based Medical Image Classification Using a Multilayer Fuzzy Approach (MFA), was validated using open access medical image data, where an image with a particular type of abnormal object contained in it was compared with a normal image with the same object in it. The similarity was then computed in percentages and subtracted from the hundred, which is the abnormality in the first image. The results showed that the novel MFA outperforms significantly better than the benchmark, CNN, and is a useful tool for automated analysis of medical image data sets.
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