RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification

Authors

  • Dhyanendra Jain Department of CSE-AIML, ABES Engineering College Ghaziabad (U.P), India
  • Sheela Hundekari School of Engineering and Technology, Pimpri Chinchwad University, Pune, India
  • Kamal Upreti Christ University, Delhi NCR Campus, Ghaziabad, India
  • Nishi Jain Vivekananda School of Engineering and Technology, VIPS-TC, Pitampura, Delhi, India
  • Monica Rose G. D. Goenka University, Gurgaon, Haryana, India
  • Nidhi Singh G. D. Goenka University, Gurgaon, Haryana, India
  • Rahul Singhai IIPS, Devi Ahilya University, Indore, India
  • Manoj Kumar Gurukula Kangri University Haridwar, Uttarakhand, India

DOI:

https://doi.org/10.13052/jmm1550-4646.2053

Keywords:

Computed tomography, data augmentation, horizontal flips, rebuild convolution block attention module-based convolutional neural network, U-Net

Abstract

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 network’s 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 model’s 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.

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Author Biographies

Dhyanendra Jain, Department of CSE-AIML, ABES Engineering College Ghaziabad (U.P), India

Dhyanendra Jain is currently working as an Associate Professor in Department of CSE-AIML, ABES Engineering College, Ghaziabad affiliated to AKTU, U.P, India. He previously worked with Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi, ITM College, Gwalior, gaining more than fifteen years of rich experience in research working in academia. He has attended national and international conferences as a session chair and been a keynote speaker in various platforms. He was awarded best teacher, best researcher, and extra academic performer. He has published patents, books and research papers in various national and international conferences and journals. His research area includes Artificial Intelligence, Machine Learning, Data Mining.

Sheela Hundekari, School of Engineering and Technology, Pimpri Chinchwad University, Pune, India

Sheela Hundekari is not merely confined to the academic realm; they have garnered acclaim on a global scale through their certifications and training endeavors. Holding five prestigious certifications, including three in Java and two in Oracle, Professor Dr. Sheela stands as a paragon of technical proficiency and industry relevance. Their designation as a NASSCOM certified trainer further solidifies their status as a leading authority in the field.

With an illustrious academic journey spanning over 25 years, Currently working with renowned University, Pimpri Chinchwad University, Pune. Professor Dr. Sheela Hundekari embodies a remarkable blend of scholarly prowess and practical expertise. Armed with a Ph.D. and a diverse array of qualifications including an MBA, MCA, and MCM, their academic voyage has been marked by an insatiable thirst for knowledge and an unwavering commitment to excellence.

In addition to their instructional prowess, Professor Dr. Sheela Hundekari is an avid researcher, with a prolific portfolio of national and international research papers to their credit. Their contributions to the academic discourse have not only enriched their respective fields but have also spurred innovation and progress.

Kamal Upreti, Christ University, Delhi NCR Campus, Ghaziabad, India

Kamal Upreti is currently working as an Associate Professor in Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He completed is B. Tech (Hons) Degree from UPTU, M. Tech (Gold Medalist), PGDM(Executive) from IMT Ghaziabad and PhD in Department of Computer Science & Engineering. He has completed Postdoc from National Taipei University of Business, TAIWAN funded by MHRD.

He has published 50+ Patents, 32+Magazine issues and 110+ Research papers in in various reputed Journals and international Conferences. His areas of Interest such as Modern Physics, Data Analytics, Cyber Security, Machine Learning, Health Care, Embedded System and Cloud Computing. He has published more than 45+ authored and edited books under CRC Press, IGI Global, Oxford Press and Arihant Publication. He is having enriched years’ experience in corporate and teaching experience in Engineering Colleges.

He worked with HCL, NECHCL, Hindustan Times, Dehradun Institute of Technology and Delhi Institute of Advanced Studies, with more than 15+ years of enrich experience in research, Academics and Corporate. He also worked in NECHCL in Japan having project – “Hydrastore” funded by joint collaboration between HCL and NECHCL Company. He has completed project work with Joint collaboration with GB PANT & AIIMS Delhi, under funded project of ICMR Scheme on Cardiovascular diseases prediction strokes using Machine Learning Techniques from year 2017–2020 of having fund of 80 Lakhs. He got 3 Lakhs fund from DST SERB for conducting International Conference, ICSCPS-2024, 13–14 Sept 2024. Recently, he got 10 Lakhs fund from AICTE – Inter-Institutional Biomedical Innovations and Entrepreneurship Program (AICTE-IBIP) for 2024–2026. He has attended as a Session Chair Person in National, International conference and key note speaker in various platforms such as Skill based training, Corporate Trainer, Guest faculty and faculty development Programme. He awarded as best teacher, best researcher, extra academic performer and Gold Medalist in M. Tech programme.

Nishi Jain, Vivekananda School of Engineering and Technology, VIPS-TC, Pitampura, Delhi, India

Nishi Jain is working as an Assistant Professor in the department of Computer Science and Engineering at Vivekananda Institute of Professional Studies – Technical Campus, India. She is currently pursuing Ph.D. in Computer Science and Engineering from Guru Gobind Singh Indraprastha University. She completed her Bachelors and Masters from Delhi Technological University and Maharshi Dayanand University. She has a teaching experience of about 3 years. Her research interests are in the area of Design and Analysis of Algorithms, Machine Learning and Deep Learning.

Monica Rose, G. D. Goenka University, Gurgaon, Haryana, India

Monica Rose is an experienced academic and researcher specializing in business analytics, decision science, and information systems. With a Ph.D. from YMCA University and certifications from IIT Delhi, she has contributed extensively to academia through roles at institutions including Amity University, GD Goenka University, and Sunway University. Monica’s teaching encompasses a wide array of subjects like Python, blockchain, and quantitative techniques. She has published widely, received best paper awards, and served on accreditation teams and quality assurance initiatives. Her expertise extends to developing courses, coordinating MBA programs, and fostering student engagement through innovative learning methods.

Nidhi Singh, G. D. Goenka University, Gurgaon, Haryana, India

Nidhi Singh is an accomplished academician with over 14 years of experience spanning teaching, training, and corporate sectors. Currently serving as an Assistant Professor at G.D. Goenka University in Haryana, India, she holds a Ph.D. in Management and an MBA in Information Technology and Marketing. Her expertise covers business analytics, information technology, and emerging fields such as machine learning, data visualization, and disruptive technologies in higher education. She has published over twenty research papers in reputed journals like SCOPUS, WOS, and ABDC. Dr. Singh also holds certifications from prestigious institutions, including Harvard Business School and the Indian Institute of Management Visakhapatnam, further solidifying her expertise in emerging technologies and business strategies.

Rahul Singhai, IIPS, Devi Ahilya University, Indore, India

Rahul Singhai is affiliated to International Institute of Professional Studies (IIPS), Devi Ahilya University, Indore (M.P.), India where he is currently working as Assistant Professor (Senior Grade). He had received his Ph.D. degree in the core area of Data Mining from Department of Computer Sc & App, Dr. Hari Singh Gour Central University, Sagar (M.P.). He has authored and co-authored several national and international publications and also working as a reviewer for reputed professional journals. He is having an active association with different societies and academies around the world. He has received grant for 3 patents in the different area of computer science. His major research interest involves Data Mining, Operating System, DBMS, Computer Network & Cloud Computing.

Manoj Kumar, Gurukula Kangri University Haridwar, Uttarakhand, India

Manoj Kumar working as an Associate Professor in the Department of Mathematics and Statistics, Gurukula Kangri University, Haridwar (Uttarakhand). He is obtained his B. Sc. Degree from Chaudhary Charan Singh University, Meerut, Uttar Pradesh. He is awarded for M Sc, M Phil and Ph D Degrees in Mathematics from the same University. He has also completed a six-month course viz certificate of Proficiency in Russian Language during his M Phil Degree program. He is qualified CSIR-NET exam in Mathematics. His fields of research interest are Cryptography and Network Security, Elliptic Curve Cryptography, Isogeny based Cryptography, Quantum Cryptography, Post Quantum Cryptography and Ancient Indian Vedic Mathematics. He has published more than 50 research articles in his research area. He has published a book entitled “The Elliptic Curves: Vedic Mathematics and Cryptography”. Five scholars have been awarded Ph D degree under his supervision. Three research scholars are still working in his guidance.

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Published

2024-12-20

How to Cite

Jain, D. ., Hundekari, S. ., Upreti, K. ., Jain, N. ., Rose, M. ., Singh, N. ., Singhai, R. ., & Kumar, M. . (2024). RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification. Journal of Mobile Multimedia, 20(05), 1039–1066. https://doi.org/10.13052/jmm1550-4646.2053

Issue

2024: Vol 20 Iss 5

Section

Articles

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