Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The
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-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.
The continual changes in requirements impact the expectation about the quality of the product as well as the process, namely software project. In this paper, the various limitations that still exist in the software development process are presented. It is aimed to develop a new quality improvement model to enhance software quality without increasing effort, cost, and time. To achieve a software project of expected quality, a new quality improvement model, namely Kano Lean Six Sigma model (KLSS), is proposed. The KLSS model is used to identify the exact requirements for the software project from the customer's perspective. KLSS helps to categorize the requirements based on the nature of the defect, to eliminate the requirements of non-value processes and to implement the main functionality to meet the expectations of the customer. As regards our proposed software maintenance project, the method of development has been suitably tested in a leading IT company. The model has shown greater improvement in quality, cost, and efforts.
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