Abstract:
Machine learning is the leading eld of arti cial intelligence that has achieved expertlevel
performance. Medical imaging has led to the advancements in diagnosis and
treatment of various medical diseases. Classi cation or identi cation of thoracic diseases
based on chest X-rays is one of the potential applications in medical imaging. The
goal of this research work is the application of Support Vector Machine in the medical
eld. The study consists of 112,120 images of 30,804 individual patients with fourteen
thoracic disease labels. The SVM distinctively a ord balanced predictive performance
and e ectively classify the diseases from image processing. To reduce the dimensionality
and outliers from the SVM are coupled with novel Fast Principal Component
Analysis (FPCA). SVM is accomplished in handling an in nite number of features
and has a mathematical function named as kernels. Kernels address the non-linearity
problem with accuracy and exibility. In this thesis, we assess the performance of four
kernels of SVM namely Linear (L-SVM), Polynomial (P-SVM), Radial Basis (R-SVM),
and Hyperbolic Tangent (H-SVM) for the classi cation of thoracic diseases based on
X-ray images. Finally, based on the accuracy these four kernels are compared with
each other and novel interpretations are also presented in the results part. It appears
there is a signi cant (p ≤ 0.05) di erence between SVM kernels where P-SVM and
R-SVM next in order outperform on most of the diseases identi cation models with
average validated classi cation accuracy ranging from 90% to 98%. The average calibrated
accuracy from 99.4% reaches 100% in most of the cases. Experimental results
exhibit that SVM performs e ciently with kernels. 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 di erent medical techniques.
