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Doctors face difficulty in the diagnosis of lung cancer due to the complex nature and clinical interrelations of computer-diagnosed scan images. Hence, the visual inspection and subjective evaluation methods are time consuming and tedious, which leads to inter and intra observer inconsistency or imprecise classification. The Computer-Aided Detection (CAD) can help the clinicians for objective decision-making, early diagnosis, and classification of cancerous abnormalities. In this work, CAD has been employed to enhance the accuracy, sensitivity, and specificity of automated detection in which, the phases of lung cancer are discriminated using image processing tools. Cancer is the second leading cause of death in non-communicable diseases worldwide. Lung cancer is, in fact, the most dangerous form of cancer that affects both the genders. Either or both sides of the lung begin to expand during the uncontrolled growth of extraordinary cells. The most widely used imaging technique for lung cancer diagnosis is Computerised Tomography (CT) scanning. In this work, the CAD method is used to differentiate between the phases of pictures of lung cancer stages. Abnormality detection consists of 4 steps: pre-processing, segmentation, extraction of features, and classification of input CT images. For the segmentation process, Marker-controlled watershed segmentation and the K-means algorithm are used. From CT images, normal and abnormal information is extracted and its characteristics are determined. Stages 1–4 of cancerous imaging were discriminated and graded with approximately 80% efficiency using neural network feedforward backpropagation algorithm. Input data is collected from the Lung Image Database Consortium (LIDC), which out of 1018 dataset cases uses 100 cases. For the output display, a graphical user interface (GUI) is developed. This automated and robust CAD system is necessary for accurate and quick screening of the mass population. |
