Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE

ABSTRACTABSTRACTThe term 'lung disease' covers a wide range of conditions that affect the lungs, including asthma, COPD, infections like the flu, pneumonia, tuberculosis, lung cancer, COVID, and numerous other breathing issues. Respiratory failure may result from several respiratory disorders. Recently, various methods have been proposed for lung disease detection, but they are not much more efficient. The proposed model has been tested on the COVID dataset. In this work, Littlewood-Paley Empirical Wavelet Transform (LPEWT) based technique is used to decompose images into their sub-bands. Using locally linear embedding (LLE), linear discriminative analysis (LDA), and principal component analysis (PCA), robust features are identified for lung disease detection after texture-based relevant Gabor features are extracted from images. LLE's outcomes inspire the development of new techniques. The Entropy, ROC, and Student's t-value methods provide ranks for robust features. Finally, LS-SVM is fed with t-value-based ranked features for classification using Morlet wavelet, Mexican-hat wavelet, and radial basis function. This model, which incorporated tenfold cross-validation, exhibited improved classification accuracy of 95.48%, specificity of 95.37%, sensitivity of 95.43%, and an F1 score of.95. The proposed diagnosis method can be a fast disease detection tool for imaging specialists using medical images.KEYWORDS: Machine learningGabormedical imagingEWTLLE AcknowledgementsI want to thank my Ph.D. supervisor and my parents.Disclosure statementNo potential conflict of interest was reported by the authors.Ethical approvalThere are no studies by the author using human subjects or animals in this article.Additional informationFundingThere was no outside funding for this study.