Lung cancer remains a leading cause of mortality globally, with outcomes heavily dependent on the timeliness and accuracy of diagnosis. Traditional medical imaging techniques, while foundational in detecting lung nodules, often falter in distinguishing malignant from benign lesions with high precision, largely due to their inability to contextualize the complex spatial relationships within the images. Precise segmentation and classification of lung nodules is crucial for the early detection of lung cancer. This paper presents a novel deep learning model that incorporates a transformer block to improve the performance of lung cancer detection and classification. From performance evaluation, it is evident that our proposed model has an average accuracy of 93%. 05%, which is superior to the existing D3DR _ MKCA model with a mean accuracy of 91.53%. These findings are especially important for the identification of Adenocarcinoma and Small Cell Carcinoma, as improvements in the precision and recall factors have been achieved in these cases.
Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Vol. Sep;20(9):624-639, Nature reviews Clinical oncology.
2.
Liao J, Li X, Gan Y, Han S, Rong P, Wang W, et al. Artificial intelligence assists precision medicine in cancer treatment. Vol. 12, Frontiers in Oncology.
3.
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA: a cancer journal for clinicians. Vol. Mar;69(2):127-157.
4.
Alamer L, Alqahtani IM, Shadadi E. Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier. Journal of Internet Services and Information Security.
5.
Mridha MF, Prodeep AR, Hoque ASMM, Islam MdR, Lima AA, Kabir MM, et al. A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. Vol. 2022, Journal of Healthcare Engineering. 2022. p. 1–43.
6.
Yin XX, Hadjiloucas S, Zhang Y, Tian Z. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. Vol. 214, Computer Methods and Programs in Biomedicine. 2022. p. 106510.
7.
Rajapaksa SM. Weakly Supervised Perturbation Based Method for 3D Brain Tumour Segmentation.
8.
Ramakrishnan J, Ravi Sankar G, Thavamani K. Publication Growth and Research in India on Lung Cancer Literature: A Bibliometric Study. Vol. 9, Indian Journal of Information Sources and Services. p. 44–7.
9.
Quanyang W, Yao H, Sicong W, Linlin Q, Zewei Z, Donghui H, et al. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Vol. 13, Cancer Medicine. 2024.
10.
Sun G, Pan Y, Kong W, Xu Z, Ma J, Racharak T, et al. DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation. Vol. 12, Frontiers in Bioengineering and Biotechnology.
11.
Zhi L, Jiang W, Zhang S, Zhou T. Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons. Vol. 164, Computers in Biology and Medicine. 2023. p. 107321.
12.
Shashikala D, Chandran CP, Rajathi S. Cross-spectral vision transformer for lung nodule detection with improved moth flame algorithm using deep learning. Vol. 8, e-Prime - Advances in Electrical Engineering, Electronics and Energy. 2024. p. 100556.
13.
Li X, Li M, Yan P, Li G, Jiang Y, Luo H, et al. Deep learning attention mechanism in medical image analysis: Basics and beyonds. International Journal of Network Dynamics and Intelligence.
14.
Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, et al. Transformers in medical imaging: A survey. Vol. 88, Medical Image Analysis. 2023. p. 102802.
15.
Odilov BA, Madraimov A, Yusupov OY, Karimov NR, Alimova R, Yakhshieva ZZ, et al. Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity. Vol. 5;9(1):72-83, Natural and Engineering Sciences.
16.
Annavarapu CSR, Parisapogu SAB, Keetha NV, Donta PK, Rajita G. A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation. Vol. 13, Diagnostics. p. 1406.
17.
Liu G, Liu F, Gu J, Mao X, Xie X, Sang J. An attention-based deep learning network for lung nodule malignancy discrimination. Vol. 16, Frontiers in Neuroscience.
18.
Wu Z, Li X, Zuo J. RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning. Vol. 13, Frontiers in Oncology.
19.
Saihood A, Karshenas H, Naghsh-Nilchi AR. Multi-Orientation local texture features for guided attention-based fusion in lung nodule classification. IEEE Access.
20.
Rao HVR, Sankar DrVR. Precision in Prostate Cancer Diagnosis: A Comprehensive Study on Neural Networks. Vol. 15, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2022. p. 109–22.
21.
Bhattacharyya D, Thirupathi Rao N, Joshua ES, Hu YC. A bi-directional deep learning architecture for lung nodule semantic segmentation. The Visual Computer.
22.
Canayaz M, Şehribanoğlu S, Özgökçe M, Akıncı MB. A comprehensive exploration of deep learning approaches for pulmonary nodule classification and segmentation in chest CT images. Neural Computing and Applications.
23.
Gai L, Xing M, Chen W, Zhang Y, Qiao X. Comparing CNN-based and transformer-based models for identifying lung cancer: which is more effective? Vol. Jun;83(20):59253-59269, Multimedia Tools and Applications.
24.
Ali H, Mohsen F, Shah Z. Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review. Vol. 23, BMC Medical Imaging.
25.
Cao Z, Li R, Yang X, Fang L, Li Z, Li J. Multi-scale detection of pulmonary nodules by integrating attention mechanism. Vol. 13, Scientific Reports.
26.
Yang S, Yang X, Lyu T, Huang JL, Chen A, He X, et al. Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models. Journal of Healthcare Informatics Research.
27.
Usman M, Shin YG. DEHA-Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation. Vol. 23, Sensors. p. 1989.
28.
Jasim WA, Mohammed RJ. A Survey on Segmentation Techniques for Image Processing. Vol. 1;17(2):73-93, Iraqi Journal for Electrical & Electronic Engineering.
29.
Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, et al. Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging. Vol. 8, Frontiers in Cardiovascular Medicine.
30.
Chen W, Wang Y, Tian D, Yao Y. Ct lung nodule segmentation: A comparative study of data preprocessing and deep learning models. Vol. 11, IEEE Access. p. 34925–31.
31.
Zheng R, Wen H, Zhu F, Lan W. Attention-guided deep neural network with a multichannel architecture for lung nodule classification. Vol. 10, Heliyon. 2024. p. e23508.
Citation
Copyright
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.