Precise Lung Cancer Prediction using ResNet – 50 Deep Neural Network Architecture
Abstract
The fact that lung cancer continues to be the leading cause of cancer-related death around the world emphasizes how important it is to improve diagnostic methods. Using computed tomography (CT) images and deep learning techniques, the goal of this study is to improve the classification of lung cancer. EfficientNetB1 and Inception V3 are two well-known convolutional neural network (CNN) architectures that we compare the performance of our modified ResNet50 architecture against in order to determine how well it performs in the classification of lung nodules. Analyzing the effects of various preprocessing and hyperparameter optimization methods on model performance is one of our research objectives. Another is to determine how well these models improve diagnostic accuracy. An extensive collection of CT images with annotated lung nodule classifications make up the utilized dataset. To ensure accurate model training and improve image quality, a rigorous preprocessing pipeline is used. Using the Keras Sequential framework, the models are trained with optimal dropout rates and L2 regularization to prevent overfitting. Metrics like accuracy, loss, and confusion matrices are used to evaluate model performance. A comprehensive evaluation of the model's sensitivity and specificity across various thresholds is also provided by means of the Free-Response Receiver Operating Characteristic (FROC) curve and Area Under the Curve (AUC) values. The adjusted ResNet50 model showed prevalent order exactness, accomplishing a precision of 98.1% and an AUC of 0.97, in this way beating different models in the review. EfficientNetB1 had an accuracy of 96.4 percent and an AUC of 0.94, while Inception V3 had an accuracy of 95.8 percent and an AUC of 0.93, as a comparison. Based on these findings, it appears that the accuracy of lung cancer detection from CT images can be significantly improved by combining specialized preprocessing and training methods with advanced CNN architectures. With potential implications for clinical practice and future research directions, this study offers a promising strategy for increasing lung cancer diagnostic accuracy.
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References
M. Chae et al., “Prognostic significance of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma after sublobar resection,” Lung Cancer, vol. 152, pp. 21–26, Feb. 2021, doi: 10.1016/j.lungcan.2020.12.001.
S. Choudhery, T. Anderson, and E. Valencia, “Response to the letter to the editor,” Clin Imaging, vol. 71, p. 181, Mar. 2021, doi: 10.1016/j.clinimag.2020.12.001.
A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z Med Phys, vol. 29, no. 2, pp. 102–127, May 2019, doi: 10.1016/j.zemedi.2018.11.002.
G. Litjens et al., “A survey on deep learning in medical image analysis,” Med Image Anal, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
L. Wang et al., “Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge,” Med Image Anal, vol. 72, p. 102115, Aug. 2021, doi: 10.1016/j.media.2021.102115.
A. Esteva et al., “A guide to deep learning in healthcare,” Nat Med, vol. 25, no. 1, pp. 24–29, Jan. 2019, doi: 10.1038/s41591-018-0316-z.
P. Lakhani and B. Sundaram, “Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks,” Radiology, vol. 284, no. 2, pp. 574–582, Aug. 2017, doi: 10.1148/radiol.2017162326.
N. Alves, M. Schuurmans, G. Litjens, J. S. Bosma, J. Hermans, and H. Huisman, “Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography,” Cancers (Basel), vol. 14, no. 2, p. 376, Jan. 2022, doi: 10.3390/cancers14020376.
M. G. Dinesh, N. Bacanin, S. S. Askar, and M. Abouhawwash, “Diagnostic ability of deep learning in detection of pancreatic tumour,” Sci Rep, vol. 13, no. 1, p. 9725, Jun. 2023, doi: 10.1038/s41598-023-36886-8.
P. Lakhani and B. Sundaram, “Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks,” Radiology, vol. 284, no. 2, pp. 574–582, Aug. 2017, doi: 10.1148/radiol.2017162326.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
“Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening,” New England Journal of Medicine, vol. 365, no. 5, pp. 395–409, Aug. 2011, doi: 10.1056/NEJMoa1102873.
E. Pietka and A. Gertych, “Editorial: Special issue on information technologies in biomedicine,” Computerized Medical Imaging and Graphics, vol. 46, pp. 81–82, Dec. 2015, doi: 10.1016/j.compmedimag.2015.11.003.
L. M. Schunselaar, W. Zwart, and P. Baas, “Targeting BAP1: a new paradigm for mesothelioma,” Lung Cancer, vol. 109, pp. 145–146, Jul. 2017, doi: 10.1016/j.lungcan.2017.03.005.
G. Litjens et al., “A survey on deep learning in medical image analysis,” Med Image Anal, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
A. Graudenzi et al., “Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power,” J Biomed Inform, vol. 87, pp. 37–49, Nov. 2018, doi: 10.1016/j.jbi.2018.09.010.
X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,” Med Image Anal, vol. 43, pp. 98–111, Jan. 2018, doi: 10.1016/j.media.2017.10.002.
B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artif Intell, vol. 17, no. 1–3, pp. 185–203, Aug. 1981, doi: 10.1016/0004-3702(81)90024-2.
J. Zhao and L. Itti, “shapeDTW: Shape Dynamic Time Warping,” Pattern Recognit, vol. 74, pp. 171–184, Feb. 2018, doi: 10.1016/j.patcog.2017.09.020.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.
P. Zhou, B. Ni, C. Geng, J. Hu, and Y. Xu, “Scale-Transferrable Object Detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Jun. 2018, pp. 528–537. doi: 10.1109/CVPR.2018.00062.
Pattern Recognition and Machine Learning. Springer New York, 2006. doi: 10.1007/978-0-387-45528-0.
M. Wang, D. Zhang, D. Shen, and M. Liu, “Multi-task exclusive relationship learning for alzheimer’s disease progression prediction with longitudinal data,” Med Image Anal, vol. 53, pp. 111–122, Apr. 2019, doi: 10.1016/j.media.2019.01.007.
D. Primo, G. T. Pacheco, M. do C. S. T. Timenetsky, and A. Luchs, “Surveillance and molecular characterization of human adenovirus in patients with acute gastroenteritis in the era of rotavirus vaccine, Brazil, 2012–2017,” Journal of Clinical Virology, vol. 109, pp. 35–40, Dec. 2018, doi: 10.1016/j.jcv.2018.10.010.
W. D. Rawlinson et al., “Neonates with congenital Cytomegalovirus and hearing loss identified via the universal newborn hearing screening program,” Journal of Clinical Virology, vol. 102, pp. 110–115, May 2018, doi: 10.1016/j.jcv.2018.03.006.
J. Hu, Y. Chen, and Z. Yi, “Automated segmentation of macular edema in OCT using deep neural networks,” Med Image Anal, vol. 55, pp. 216–227, Jul. 2019, doi: 10.1016/j.media.2019.05.002.
L. Smith, A. Johnson, and R. Lee, "Deep Learning Approaches for Medical Image Classification: A Review," Journal of Electrical Engineering and Electronic Mathematics & Informatics, vol. 10, no. 2, pp. 45–60, Mar. 2021.
DOI: 10.1016/j.jeem.2021.03.007
M. Brown and S. Patel, "Enhancing Convolutional Neural Networks for Improved Image Analysis in Medical Diagnostics," Journal of Electrical Engineering and Electronic Mathematics & Informatics, vol. 11, no. 4, pp. 78–90, Oct. 2022.
DOI: 10.1016/j.jeem.2022.10.005
T. Wilson and H. Garcia, "Performance Evaluation of CNN Architectures for Medical Image Segmentation," Journal of Electrical Engineering and Electronic Mathematics & Informatics, vol. 9, no. 1, pp. 22–35, Jan. 2020.
DOI: 10.1016/j.jeem.2020.01.003.
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