3D Medical Image Classification with Depthwise Separable Networks

Abstract

This research studies a dilated depthwise separable convolution neural network (DSCN) model to identify human tissue types from 3D medical images. 3D medical image classification is a challenging task due to the unpredictable noise and indistinct tissue behaviors of the image content. The objective of this research is to improve typical supervised deep learning model accuracy by using dilated convolution and depthwise separable network approaches on 3D medical image classification tasks. A depthwise separable architecture is used to improve parameter utilization efficiency. Dilated convolutions are applied to systematically aggregate multiscale contextual information and provide a large receptive field with a small number of trainable weights. The performance of the constructed model is tested to perform a multi-class human tissue classification on 3D Optical Coherence Tomography (OCT) images. Experimental results are compared with typical deep learning classification models. The results show that the DSCN model outperforms other models for all the tissue classification tasks. The proposed DSCN model can be a potential approach for the 3D image-based diagnostic tasks in both healthcare and manufacturing field.