Radiology Image Data Augmentation and Image Enhancement in Respiratory Disease Infection Detection Using Machine Learning Approach

Abstract

Medical imaging plays an important role in medical diagnosis and treatment. It is also useful in medical applications. The proposed concept's goal is to understand the importance of data balancing, data augmentation, and segmentation in the clinical field, to improve image data balancing using data augmentation and edge detection techniques, to improve radiology image preprocessing to locate regions of interest (ROI), and to construct custom-built Deep Neural Networks (DNN) in diagnosing respiratory illness using Machine Learning approaches. Images of varying quality from multiple machine types are frequently included in different datasets. This study used four datasets, three of which are online datasets from Kaggle and the fourth is real-time radiology pictures of COVID and Pneumonia-infected persons from neighboring local hospitals. We proposed RESP_DATA_BALANCE for image data balance in dataset construction, and RDD_ROI (Respiratory Disease Detection Region of Interest) algorithm, which combines improved image feature extraction technique using a GLCM and unsupervised K-means clustering for segmentation to identify the region of interest in the detection of respiratory diseases. Our suggested custom-built 28-layer Respiratory Disease Detection Deep Neural Network (RDD_DNN) is used for further training, testing, and validation. Furthermore, experimental results focus on performance characteristics using various data augmentation, edge detection, and preprocessing strategies.  The experimental purpose of our research study is to aid in the classification and early diagnosis of respiratory disorders.