Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

• Published in: Applied sciences Vol. 9; no. 11; p. 2329
• Main Authors: Paing, May Phu, Hamamoto, Kazuhiko, Tungjitkusolmun, Supan, Pintavirooj, Chuchart
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2019
• Subjects: Archives & records; Artificial intelligence; automatic diagnosis; Automation; Back propagation networks; backpropagation neural network; Classification; Computed tomography; Consortia; Diagnostic software; Diagnostic systems; Disease; Gene expression; Genomics; Image databases; Image detection; Image processing; Lung cancer; Lung nodules; Lymphatic system; Machine learning; Medical diagnosis; Medical imaging; Morbidity; Mortality; Neural networks; Nodules; staging; Support vector machines; Tumors; Watersheds
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9112329

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.