Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification Subject The topic of the resource Computed tomography; data augmentation; horizontal flips; rebuild convolution block attention module-based convolutional neural network; U-Net Description An account of the resource Lung cancer remains the leading cause of cancer-related deaths worldwide. Pulmonary nodules, indicative of tumor growth, present significant diagnostic challenges due to their varying sizes and shapes. Computed Tomography (CT) is commonly used for lung cancer screening due to its high sensitivity and efficacy in detecting these nodules. However, differentiating between benign and malignant nodules can be difficult due to their overlapping characteristics. To address this challenge, we propose a Rebuild Convolution Block Attention Module-based Convolutional Neural Network (RCBAM-CNN) designed to accurately classify lung nodules from CT scans. The RCBAM-CNN integrates a Rebuild Convolution Block Attention Module (RCBAM), which includes reshaped layers and redefined spatial attention mechanisms to enhance the networks focus on relevant features while minimizing noise. The performance of the proposed method is evaluated using the LIDC-IDRI dataset. Data augmentation techniques, including rotation, rescaling, and both vertical and horizontal flips, are applied to improve the models robustness and generalization. Subsequently, U-Net is employed for precise image segmentation, ensuring accurate delineation of nodule regions. The proposed RCBAM-CNN demonstrates exceptional performance, achieving an accuracy of 99.72%, surpassing existing methods such as adaptive morphology with a Gabor Filter (GF) and Capsule Network-based CNN. This approach represents a significant advancement in lung nodule classification, offering improved diagnostic accuracy and reliability. 2024 River Publishers. Creator An entity primarily responsible for making the resource Jain D.; Hundekari S.; Upreti K.; Jain N.; Rose M.; Singh N.; Singhai R.; Kumar M. Source A related resource from which the described resource is derived Journal of Mobile Multimedia, Vol-20, No. 5, pp. 1039-1066. Publisher An entity responsible for making the resource available River Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13052/jmm1550-4646.2053" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2053</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214582888&amp;doi=10.13052%2Fjmm1550-4646.2053&amp;partnerID=40&amp;md5=705a1cdd08346132bd88536691f933bf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214582888&amp;doi=10.13052%2fjmm1550-4646.2053&amp;partnerID=40&amp;md5=705a1cdd08346132bd88536691f933bf</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15504646 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain D., Department of CSE-AIML, ABES Engineering College, U.P, Ghaziabad, India; Hundekari S., School of Engineering and Technology, Pimpri Chinchwad University, Pune, India; Upreti K., Christ University, Delhi NCR Campus, Ghaziabad, India; Jain N., Vivekananda School of Engineering and Technology, VIPS-TC, Delhi, Pitampura, India; Rose M., G. D. Goenka University, Haryana, Gurgaon, India; Singh N., G. D. Goenka University, Haryana, Gurgaon, India; Singhai R., IIPS, Devi Ahilya University, Indore, India; Kumar M., Gurukula Kangri University, Uttarakhand, Haridwar, India