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Lung carcinoma disease is a serious leading death every day. Early recognition and treatment can save cancer affected the human’s life. Lung carcinoma is the most hazardous and dangerous disease. Computed Tomography scanning images are the most commonly using scanning technology in the health care industry, Computed Tomography scan images are used by doctors to analyze and identify the cancerous lesion present in the lung CT scanned images. The CAD system is a powerful tool for the radiologist to diagnosis the lesion cells in an accurate way. Computer-aided techniques and machine learning techniques use digital image processing methods to implement CAD systems. The prime aim of this investigation and experimentation is to analyze the different CAD techniques, recognizing the best-developed methods and collecting their disadvantages, faults. Analyzing the drawbacks from the previously proposed model, we are implementing new techniques to overcome the drawbacks analyzed with best accuracy results. For developed methods, the feasibility study is carried on each and every step, improvements can be made were listed out. The analysis shows that having a few low accuracies, and some have high accuracy but not having 100% accuracy. In this research, the K-means algorithm is used to segment the images. K-means algorithm is unsupervised learning, which is used when we have an unlabeled dataset, the project aims to increase the accuracy of up to 100%.
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