Towards an effective model for lung disease classification Using Dense Capsule Nets for early classification of lung diseases

Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases. However, the discovery of lung disease through X-rays is a significantly challenging task depending on the availability of skilled radiologists. There has been a recent increase in attention to the design of Convolution Neural Networks (CNN) models for lung disease classification. A considerable amount of training dataset is required for CNN to work, but the problem is that it cannot handle translation and rotation correctly as input. The recently proposed Capsule Networks (referred to as CapsNets) are new automated learning architecture that aims to overcome the shortcomings in CNN. CapsNets are vital for rotation and complex translation. They require much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We have used convolution blocks that take input images and generate convolution layers used as input to capsule block. There are 12 capsule layers operated, and the output of each capsule is used as an input to the next convolution block. The process is repeated for all blocks. The experimental results show that the proposed architecture yields better results when compared with the existing CNN techniques by achieving a better area under the curve (AUC) average. Furthermore, DNet checks the best performance in the ChestXray-14 data set on traditional CNN, and it is validated that DNet performs better with a higher level of total depth. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Machine learning and computer vision have played important roles in fighting against the COVID-19 pandemic. Radiology has significantly improved the diagnosis of diseases, especially lung diseases. Chest X-rays have become commonly used to detect and diagnose various lung diseases. However, identifying lung disease through X-rays is a challenging task that relies on skilled radiologists. Recent attention has been focused on Convolution Neural Networks (CNN) models for lung disease classification. CNN requires a large amount of training data, but it struggles with translation and rotation inputs. Capsule Networks (CapsNets) have been proposed as a solution to this problem, as they can handle rotation and complex translation. They require less training data, which is beneficial for medical image datasets like chest X-rays. This research explores the adoption and integration of CapsNets in chest X-ray classification, aiming to design a deep model that improves classification accuracy.