Abstract
Cancer is among the most terrible diseases that expand per year and eventually kill many individuals. Its most commonly diagnosed with the highest number of deaths of all diseases is lung carcinoma. The newly disclosed findings are calculated through the identification of lung cancer during this article. The study examines the lung areas with median, Gaussian, Gabor, and Otsu that contains an effective approach of deep learning for efficient use of the fully connected SqueezeNet virtualization with lung pixels and took into account a benevolent or harmful feature through all the extraction of rigorous prediction. The accuracy of KNN, LDA, SVM, and GNB are 96.37%, 93.01%, 92.32%, and 70.65%, respectively, by SqueezeNet. It demonstrates a decrease in the time of performance test using SVM; 0.096 s; 93.63, 98.36, 1.0, and 74.34% by SVM, LDA, KNN, and GNB with a 70–30 training ratio. The percentile of nodule detection is 92.90% and the minimal false-positive detection rate for KNN is zero whereas the maximum of 30.52 for GNB. The cumulative sensitivities of KNN and LDA remained 96.84% higher. Lung cancer at the highest detection rate of 96.37% at TTR ratios of 90–10 can be identified by DICOM 512 × 512 and classifiers with SqueezeNet. Accuracy of classification; vector support machines gave the best results according to data.
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Kumar, V., Bakariya, B. (2022). Detection of Lung Malignancy Using SqueezeNet-Fc Deep Learning Classification Technique. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-5747-4_59
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DOI: https://doi.org/10.1007/978-981-16-5747-4_59
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