RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification
- Title
- RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification
- Creator
- Jain D.; Hundekari S.; Upreti K.; Jain N.; Rose M.; Singh N.; Singhai R.; Kumar M.
- Description
- 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.
- Source
- Journal of Mobile Multimedia, Vol-20, No. 5, pp. 1039-1066.
- Date
- 2024-01-01
- Publisher
- River Publishers
- Subject
- Computed tomography; data augmentation; horizontal flips; rebuild convolution block attention module-based convolutional neural network; U-Net
- Coverage
- 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
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 15504646
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Jain D.; Hundekari S.; Upreti K.; Jain N.; Rose M.; Singh N.; Singhai R.; Kumar M., “RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13379.