An Effective Medical Image Classification: Transfer Learning Enhanced by Auto Encoder and Classified with SVM

Abstract

The count of white blood cells is vital for disease diagnosis, which is exploited to identify many diseases like infections and leukemia. COVID-19 is another critical disease which should be detected and cured immediately. These diseases are better diagnosed using radiological and microscopic imaging. A clinical experience is required by a physician, to identify and classify the Chest X-rays or the microscopic blood cell images. In this study a novel approach is proposed for classifying medical images by using transfer learning method which is ResNet-50 where features are reduced with Auto Encoder (AE) and classified with a Support Vector Machine (SVM) instead of softmax classifier which is tested with different optimizers. The proposed method is compared with VGG-16 and ResNet-50, Inception-V3 which use softmax classifiers. Experimental results indicated that the proposed method possess 97.3% and 99% accuracy on WBC and COVID-19 datasets respectively which are higher than compared methods for each dataset.