Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification

AN APPROACH TOWARD TAGGING UNLABELED MEDICAL DATASET

Abstract

Deep learning approaches rely on a wide-scale labeled dataset to attain a high level of performance. Although labeled data is more difficult and costly to access in some applications, such as bioinformatics and medical imaging, wide variety of ongoing research on the topic of Semi-Supervised Deep Learning (SSDL) can improve and fix underlying problems in this domain. The motivation for the suggested model Rank Based Two-Stage Semi-Supervised Deep Learning (RTS-SS-DL) is the same as how doctors deal with unobserved or suspect cases in day to day practice. The physicians deal with these suspect instances with the help of professional assistance from their colleagues. Before beginning therapy, some patients seek the opinion of a variety of skilled professionals. The patients are treated by the most appropriate (vote count) professional diagnosis. Our model (RTS-SS-DL) has achieved impressive metrics including 92.776% accuracy, 97.376% specificity, 86.932% sensitivity, 96.192% precision, 85.644% MCC (Matthews Correlation Coefficient), 3.808% FDR (False Discovery Rate), 2.624% FPR (False Positive Rate), 91.072% f1-score, 90.85% NPV (Negative Predictive Value), and 13.068% FNR (False Negative Rate) for the unseen dataset. The outcome of this research results in an SSDL model that is both more precise and effective.