Virus Texture Classification of TEM Images Using Fusion of Chebyshev Moments and Resnet50 Features

Abstract

Abstract Classification of viruses is an essential step before suitable treatment of a disease. In this work, a novel approach for the classification of virus texture from TEM images is proposed. Chebyshev moments are used to classify virus textures with the Pre-trained convolution neural networks. Resnet50 is a pre-trained deep learning model used to classify images into object categories. Chebyshev moments and the pruned features extracted from the last global pooling layer of Resnet50 are fused to improve the classification accuracy. The fused feature vector along with corresponding labels is used to train a multiclass Error Correcting Output Code (ECOC) classifier to give the output. The proposed approach is tested on a standard benchmark virus texture dataset. Peak, mean, and median classification accuracies are calculated and compared with the state of the art approaches. In addition, various other classification metrics i.e., sensitivity, specificity, Mathews Correlation Coefficient, and kappa are given to justify the validity of the proposed method. 80% of the image dataset is chosen for training and the remaining 20% for testing. A peak classification accuracy of 90.33%, mean accuracy of 86.99% and median accuracy of 86.66% is achieved. Superiority of the proposed method is justified with simulations.

Keywords

• Virus Texture classification
• Chebyshev Moments
• ECOC Classifier
• Resnet50
• Convolutional Neural Network.