LUNG DISEASE DETECTION WITH K-NEAREST NEIGHBORS ALGORITHM (International Journal of Engineering Management Science, 2019)
Among the major causes of death and illness worldwide are respiratory illnesses such aspneumonia, tuberculosis (TB), lung cancer, and chronic obstructive pulmonary disease(COPD), and thus accurate and correct diagnosis is required to provide proper therapytreatment. The traditional diagnostic procedures—X-ray imaging, computed tomography(CT) imaging, and pulmonary function tests—depend on the experience of the clinician andthus may lead to diagnostic discrepancy and time wastage. For automation and precision inlung disease detection, machine learning techniques—in the form of the K-NearestNeighbors (KNN) algorithm—have been extensively integrated into computer-aideddiagnostic (CAD) systems in recent years. The application of the KNN algorithm for lungdisease classification using clinical factors and radiographic images is explored in thiswork. X-ray and CT scan images and respective diagnostic labels constitute the modeltraining set. Feature extraction techniques such as the Histogram of Oriented Gradients(HOG) and Gray Level Co-occurrence Matrix (GLCM) are utilized to enhance diseasespecific features prior to KNN classification. With a 94.2% classification rate, results demonstrate that KNN outperforms traditional rule-based diagnosis systems. Further, theauthors of the article suggest in the paper feature selection, data augmentation, andparameter optimization to enhance model performance. Since results demonstrate, KNNcan be utilized as an effective tool for radiologists in early diagnosis as it can identifypulmonary disorders.
Keywords: Tuberculosis (TB), Computed Tomography (CT), X-ray Imaging, K-Nearest Neighbors (KNN)