Classification of Thoracic Diseases Based on Chest X-ray Images Using Kernel Support Vector Machine

Abstract

Machine learning is the leading field of artificial intelligence that has achieved expert-level performance. Diagnosis and treatment of various medical diseases have led to advancements in medical imaging. Chest X-ray-based thoracic disease classification or identification is one of the potential applications in medical imaging based on machine learning. The study consists of 112,120 images of 30,804 individual patients with fourteen thoracic disease labels, which encapsulated the support vector machine (SVM). We have considered 04 kernels in SVM, namely, linear (L-SVM), polynomial (P-SVM), radial basis (R-SVM), and hyperbolic tangent (H-SVM) for classification of thoracic diseases based on X-ray images. To reduce the dimensionality and outliers from the SVM, variants are coupled with novel fast principal component analysis (FPCA). It appears that there is a significant p ≤ 0.05 difference between SVM variants where P-SVM and R-SVM next in order outperforms on most of the disease identification models with average validated classification accuracy ranging from 92% to 98%. The average calibrated accuracy ranges from 99.5% and reaches to 100% in most of the cases. The study is worth investigating as it is good for radiologists as they will be able to classify the diseases and it will help in improving and enhancing different medical techniques.