Automated texture‐based characterization of fibrosis and carcinoma using low‐dose lung CT images

Lung cancer is the most common cause of cancer deaths worldwide and account for 1.38 million deaths per year. Patients with lung cancer are often misdiagnosed as pulmonary tuberculosis (TB) leading to delay in the correct diagnosis as well as exposure to inappropriate medication. The diagnosis of TB and lung cancer can be difficult as symptoms of both diseases are similar in computed tomography (CT) images. However, treating TB leads to inflammatory fibrosis in some of the patients. There comes the need of an efficient computer aided diagnosis (CAD) of the fibrosis and carcinoma diseases. To design a fully automated CAD for characterizing fibrous and carcinoma tissues without human intervention using lung CT images. The 18 subjects in this study include seven healthy, two fibrosis and eight carcinoma, and one necrosis cases. The dataset is built by CT cuts representing healthy is 113, fibrosis is 103, necrosis is 39, and carcinoma is 185 totalling 440 images. The gray‐level spatial dependence matrix and gray level run length matrix approach are used for extracting texture‐based features. These features are given to neural network classifier and statistical classifier. These classifier performances are evaluated using receiver‐operating characteristics (ROC). The proposed method characterizes these tissues without human intervention. Sensitivity, specificity, precision, and accuracy followed by ROC curves were obtained and also studied. Thus, the proposed automated image‐based classifier could act as a precursor to histopathological analysis, thereby creating a way to class specific treatment procedures.