Classification of Lung Disease using Local and Global Descriptors

Abstract

Recent trends indicate that instances of chronic respiratory diseases are on the rise in India mainly due to vehicular pollution, air and dust pollution, habit of smoking and also increased population. A World Health Organization report indicates that India has a ranking number one in the world for lung disease deaths. Respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), Interstitial Lung Disease (ILD), pneumonia, tuberculosis (TB) are emerging as most important health problems in the country. The proposed work is aimed at establishing more advanced diagnostic strategy for lung diseases using CT scan images. Lung diseases such as Emphysema, Pneumonia, Bronchitis are classified using CT scan images which is collected from National Biomedical Imaging Archive (NBIA). A total of 366 images are used, out of which 300 images are used for training and 66 images are used for testing. The classification task carried out with classifier support vector machine (SVM) using Histogram of Oriented Gradient (HOG)-global descriptors and Local Binary Pattern (LBP)local descriptors. The performance of the model built using Support Vector Machine indicates that it is effective in the prediction of lung disease with 98% predictive accuracy.

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

“…The classification task carried out with classifier using Histogram of Oriented Gradient (HOG) -global descriptors and Local Binary Pattern (LBP)local descriptors. The prediction performance of the lung disease achieved 98% predictive accuracy [9].…”

“…The HOG detector depends on the window overlap principle by default, the HOG blocks typically overlap such that each cell contributes more than once to the final feature descriptor. Adjacent neighboring blocks are overlapping by eight pixels both horizontally and vertically [9,10,19].…”

“…Shrinking encircling mechanism: This method is achieved by reducing the value of a in Eq. (9). In this approach, the new position of a search agent can be defined by the historical position and it achieves the best one.…”

“…In recent years, machine learning techniques have been widely employed in the medical domain to support decision-making [4][5][6][8][9][10][11]. Moreover, medical decision support systems are in high demand to automatically detect these unexpected changes correctly and classify the brain MRI as normal or any class of disease [2][3][4][5][6].…”

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

“…The prevalence of asthma is at a higher rate in elderly men living in urban areas as compared to elderly women and rural areas. Many studies showed that stress can also worsen asthma [25,26]. Heart diseases include heart attack, stroke, cardiac arrest, high blood pressure, peripheral artery disease, cardiovascular condition, and heart failure.…”