Abstract
Introduction
A critical step to ameliorate diagnosis and extend patient survival is Benign-malignant Pulmonary Nodule (PN) classification at earlier detection. On account of the noise of Computed Tomography (CT) images, the prevailing Lung Nodule (LN) detection techniques exhibit broad variation in accurate prediction.
Methods
Thus, a novel Nodule Detection along with Classification algorithm for early diagnosis of Lung Cancer (LC) has been proposed. Initially, employing the Adaptive Mode Ostu Binarization (AMOB) technique, the Lung Volumes (LVs) isextortedas of the image together with the extracted lung regions is pre-processed. Then, detection of LNs takes place, and utilizing Geodesic Fuzzy C-Means Clustering (GFCM) Segmentation Algorithm, it is segmented.Next, the vital features are extracted, and the Nodules are classified by utilizing Logarithmic Layer Xception Neural Network (LLXcepNN) Classifier grounded on the extracted feature.
Results
The nodules are classified as Benign Nodules (BN) and Malignant Nodules (MN) by the proposed classifier. Lastly, the Lung CT images are scrutinized.
Discussion
Thus, when weighed against the prevailing techniques, the proposed systems’ acquired outcomes exhibit that the rate of accuracy of classification is enhanced.
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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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We thank the anonymous referees for their useful suggestions.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by VKG, SB. The first draft of the manuscript was written by VKG and all authors commented on previous versions of the manuscript.All authors read and approved the final manuscript.
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Gugulothu, V.K., Balaji, S. An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier. J Cancer Res Clin Oncol 149, 6049–6057 (2023). https://doi.org/10.1007/s00432-022-04539-4
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DOI: https://doi.org/10.1007/s00432-022-04539-4