X-rays imaging analysis for early diagnosis of the thoracic disorders using Capsule Neural Network with Transfer learning

Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize.

I. Introduction

The area of the body between the neck and the belly is known as the thorax. It contains vital organs like the heart, major blood vessels, and lungs and is supported by the ribs, breastbone, and spine. The diaphragm is a little muscle that separates the thorax from the abdomen. Thoracic ailments are a broad category that includes a variety of problems that may prevent the chest from working normally. Additionally, the pulmonary, respiratory, and breathing capacity functions may be hampered by these diseases. Thoracic diseases like lung cancer, mesothelioma, and asthma, some of which are brought on by bacterial, viral, or fungal infections, are linked to environmental factors.