Lung Disease Identification Based on Chest X-ray and Lung Sounds Using Machine Learning and Deep Learning Techniques

Abstract

In recent years there is heavy demand in healthcare systems due to COVID-19 pandemic. The COVID-19 will mainly cause the Lung infections, which affected the whole world very badly during last two years and still continues to affecting the world, this causes problem to the life of common man.  To overcome such problems and also in order to identify the type of the lung disease, and diagnosing abnormalities in the lung area the Chest X-Rays (CXRs) and Lung Sounds are most commonly used medical testings. Accurate identification of diseases helps in saving the life of a human from diseases like covid-19, pneumonia, TB, lung cancer etc. The commonly used medical testings are cost effective and which are very helpful in early diagnosis of pulmonary diseases. The most difficult task for radiologists and pulmonologists is to classify the pulmonary diseases using images of X-rays and Lung sounds. To identify the lung diseases, Computer Aided Diagnosis (CAD) systems assist doctors in identifying underlying diseases. Due to less availability of skilled radiologists and lung sound recording devices will make the situation of the patients more worse. The goal is to resolve the problem using non clinical methods such as Machine and Deep Learning Techniques and these techniques may be very helpful in proper detection of severe respiratory diseases using lung sounds and lung X-ray images. Lung sounds provides better accuracy and also the proposed work provides the precautionary measures to prevent the Lung infections. Hence using usual medical testings and efficient techniques are capable to overcome the severity of lung diseases. So the work aims in identify the type of the Lung disease by employing the machine learning techniques viz. fuzzy logic and Convolutional neural network (CNN) in deep learning for improvement of the performance/accuracy.