Abstract
This paper suggests a lung cancer detection system with marked and unmarked nodules of cancerous elements that are detected and classified. Identification of lung cancer in earlier stage will reduce the cause of death, and lung cancer is said to be one of the leading causes of death. Computed tomography (CT) is used for lung cancer analysis and diagnosis, and manual process suffers from several challenges such as poor accuracy. There are numerous research contributions in this area but research attempt toward robustness is all-time challenge. We have implemented a fuzzy inference system, which includes four important stages as preprocessing, image segmentation, feature extraction, and design of fuzzy inference rules. These rules are used to identify cancerous cells accurately.
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Tiwari, L., Raja, R., Sharma, V., Miri, R. (2020). Fuzzy Inference System for Efficient Lung Cancer Detection. In: Gupta, M., Konar, D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_4
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DOI: https://doi.org/10.1007/978-981-13-8798-2_4
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