In the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low‐frequency changes in image intensity that arise from the sensor and the person's movement while recording the C.T. scanned images. Although numerous machine learning techniques are used to improve the effectiveness of automatic lung cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real‐time requirement of the diagnostic situations. A new extreme learning machine (ELM) algorithm‐based model (hereafter called XlmNet) is proposed to classify the histopathology scans effectively. XlmNet utilizes The Cancer Imaging Archive (TCIA) dataset. After data collection, the initial stage in XlmNet is preprocessing, including noise removal, histogram equalization, and quality‐improved image. The enhanced Profuse Clustering (EPC) method is implemented for segmenting the affected regions from C.T. scans by image segment using superpixel clustering. The statistical attributes are extracted by using Principal Component Analysis (PCA). ELM classifier helps in classifying the lung nodules. The empirical results of the XlmNet model are related to some advanced classifiers concerning performance metrics. The evaluations of XlmNet on the TCIA dataset reveal that XlmNet outperforms other classification networks with the Accuracy of 0.965, a sensitivity of 0.964, a specificity of 0.865, a precision of 0.962, a Jaccard similarity score (JSS) of 0.95.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.