Performance of Image Processing Algorithm for Lung Cancer Classification

The deadliest red and prevalent disease responsible for huge death cases every year is cancer. In various cancers, the highly predominant cancer-associated towards most incredible mortality ratio is lung cancer. Thereby, to identify or detect lung cancer, the utilization of computed tomography (CT) scans is essential as that presents a comprehensive image of tumor located in the body and its evolution tracking. However, even though CT prefers different imaging modalities, the visual interpretation of such CT scan images may result in erroneous function, causing lag in detecting lung cancer. Consequently, image processing methods have been extensively applied in the medical domain intended for the detection of lung tumor in formative years. Over the past years, extensive studies have been effectuated towards the CAD system for lung cancer detection employing CT images. Generally, the entire process involves four stages: pre-processing or lung segmentation, nodule detection, nodule segmentation, and classification. The study presents performance execution and investigation of the image processing technique for the detection of lung cancer. In this research, a detection technique of lung cancer attributed to image segmentation is proposed since it is halfway in image processing. Further, the approach is extended to Marker control watershed and region growing methodology for segmenting of CT scan image. With the image enhancement followed towards the phases of detection. In this light, the detection segments outcome using Gabor filter, image segmentation, and features extraction. With the assistance of experimental results, it is observed that the efficacy of the proposed approach has reached the highest mark for detecting foremost topographies of lung cancer through the watershed by masking technique attaining extreme precision and vigorous.