Design Algorithm for Detection and Quantification of Lung Cancer Using Computed Tomography

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The purpose of this work is to develop detection System for detecting and precisely quantifying abnormal nodule of lung. The aim of this study is to develop a new quantitative image feature analysis scheme. We developed a computer-aided detection scheme to segment lung tumors and computed tumour-related image features. By using chest computed tomography images. Small pulmonary nodules are a common radiographic finding that presents an important diagnostic challenge in contemporary medicine.

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Lung cancer represents a major health problem. Worldwide, lung cancer is responsible for 1.3 million deaths annually, according to the WHO. Tomographic imaging modalities like multi detector X-ray, computed tomography (CT) play an important role in diagnosis, treatment, and research of lung cancer. State-of-the-art CT imaging technology enables physicians to create high-resolution volumetric scans describing lung anatomy and pathology. Higher resolution benefits diagnostic capabilities, but on the other hand, the increased amount of image data to be analyzed represents a burden for physicians. To address this problem, automated lung image analysis methods are required.

Computer-aided detection (CAD) systems have been developed toward that objective in other medical imaging modalities. They were shown to increase detection performance, improve diagnostic decisions in clinical practice and reduce the intra- and inter-subject variability for mammography and lung nodule detection in CT scans for example some studies focus on prototyping computer-aided diagnosis (CADi) systems, where the radiologist interviews the system on pre-selected suspicious area of interest. Other studies design computer aided detection (CADe) systems that output the probability map of cancer from the input image, thus combining the challenging task of localization and classification of abnormalities on a per voxel basis.

There are a lot of different detection techniques, like non invasive or biopsy techniques, currently used for early lung cancer detection. The common detection techniques for lung cancer are classical imaging techniques like chest radiography (film or digital) and computed tomography (CT).Digital radiography provides better contrast resolution with equal or better spatial resolution when compared to classical radiography techniques. However, these techniques still do not provide definitive information that can be utilized toward the early detection of tumors. Low-dose spiral/helical CT can be a promising modality for lung cancer screening. However, itis limited to small peripheral lesions. Heavy smokers develop tumors located in the central airways, and as a result, other techniques are needed for early detection.

Cancer refers to the abnormal growth of cells anywhere in the body; which tends to Prolife rate in an uncontrolled way. Many cancers and the abnormal cells which compose it are further identified by the name of the tissue that the abnormal cells originated from, for example, breast cancer, lung cancer, colon cancer, prostate cancer, and so on. Lung cancer is a leading cause of death worldwide.

Lung cancer refers to the uncontrolled growth of abnormal cells in the lung. Typically, a computed tomography (CT) scan of the thorax is the most sensitive method for detecting lung nodules and the surrounding structures. A CT scan is a painless, noninvasive diagnostic imaging procedure which creates precise multiple images (slices) of the body structures, such as the lungs [3]. The cross-sectional images generated during a CT scan can be reformatted in multiple planes, and can generate 3D images. The national lung screening trial (NLST) has shown a relative risk reduction in lung cancer-specific mortality of 20% and 6.7% in all-cause mortality using low dose CT screening.

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Design Algorithm for Detection and Quantification of Lung Cancer Using Computed Tomography. (2020, Mar 23). Retrieved January 29, 2023 , from

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