A Novel Hybrid Approach: Capsule Network Enhanced with Residual Block and CBAM Module for Medical Image Classification

Abstract

Convolutional neural networks (CNN) have been successfully used in the classification of medical images. However, CNNs extract the features of the image, neglecting the spatial information between the objects since they evaluate each object separately and this makes it difficult to analyse images reliably. CapsNets partially overcomes these limitations by encapsulating the objects in the image and their feature parameters without losing the spatial information between objects. However, this advantage of CapsNet can become a disadvantage due to the fact that CapsNet stores a lot of information, which can lead to an increase in the number of parameters and the complexity of the network. In this study, a new method is proposed in which residual block and CBAM (Convolutional Block Attention Module) modules are used hybridly with CapsNet in order to classify medical images with high accuracy and reliability and, at the same time, to overcome this disadvantage of CapsNet by reducing the number of parameters. In this study, more features of the image are extracted by using the residual block instead of the convolutional layer of CapsNet. The attention mechanism is used to increase the effectiveness of important features and reduce the effect of unnecessary features. In addition, the residual block and CBAM module reduced the number of parameters in CapsNet by approximately 2 million. Brain tumour images and OCT (Optical Coherence Tomography) images were classified with the proposed model. The results of the study show that the proposed model achieves significant results when compared to other CapsNet-based studies in the literature.