Abstract
Detection of nodules remains a very worrying problem because of its changeable shapes and sizes. Deep learning (CNN architecture) can detect the cancerous nodules at an early stage as it uses different approaches for solving problem, and are good enough to find the typical features of nodules is implemented, because deep learning methods are good to acquire characteristics from raw image data and provide proper answer to this kind of problem. Also by using KNN, better performance and accurate results are obtained. Confusion matrix for both methods is plotted to correlate how accurate both methods are. The performance measures like the classification rate , the FP rates, accuracy, and sensitivity are analyzed. Publicly available database having a wide range of data known as LIDC_IDRI database particularly for pulmonary lung nodule detection that focuses on an evaluation of automatic nodule detection.
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Patnaik, V., Mishra, C. (2021). Lungs Nodule Prediction Using Convolutional Neural Network and K-Nearest Neighbor. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_7
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DOI: https://doi.org/10.1007/978-981-16-0695-3_7
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