Deep Learning-Based Lung Medical Image Recognition

Xinghui Fei; Yulu Wang; Lu Dai; Mingxiu Sui

doi:10.55524/ijircst.2024.12.3.16

Abstract

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their detection and classification essential. The prevalent use of transfer learning in recognition algorithms often encounters a significant disparity between source and target datasets, which hampers effective feature extraction from pulmonary nodules and degrades performance. An enhanced neural network model leveraging convolutional neural networks is introduced to address this issue. This model integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer, improving the network’s ability to extract features. To ascertain the optimal configuration, the models were evaluated based on accuracy in various combinations. The experiments conducted on the LUNA16 pulmonary nodule dataset revealed that the refined network model achieved an accuracy of 88.78% and a sensitivity of 87.18%. This represents an increase of 2.7 and 2.22 percentage points in accuracy and sensitivity, respectively, compared to the GoogLeNet Inception V3 algorithm. Further tests across different dataset proportions also yielded superior outcomes, demonstrating enhanced generalization capabilities. These findings can offer objective benchmarks for clinical diagnosis.

Keywords

Deep Learning, Target Detection, Neural Network, Medical Image, Transfer Learning, GoogLeNet

References

[1] Y. Yang, A. Gilliam, E. B. Ludmir, and K. Roberts, "Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases," arXiv preprint arXiv:2403.17135, 2024.

[2] Y. Zi, Q. Wang, Z. Gao, X. Cheng, and T. Mei, "Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction," Academic Journal of Science and Technology, vol. 10, no. 2, pp. 8-12, 2024.

[3] Y. Yang, S. Jayaraj, E. Ludmir, and K. Roberts, "Text Classification of Cancer Clinical Trial Eligibility Criteria," in AMIA Annual Symposium Proceedings, vol. 2023, p. 1304, American Medical Informatics Association, 2023.

[4] S. Lu, Z. Liu, T. Liu, and W. Zhou, "Scaling-up medical vision-and-language representation learning with federated learning," Engineering Applications of Artificial Intelligence, vol. 126, p. 107037, 2023.

[5] K. Li, A. Zhu, W. Zhou, P. Zhao, J. Song, and J. Liu, "Utilizing Deep Learning to Optimize Software Development Processes," arXiv preprint arXiv:2404.13630, 2024.

[6] Y. Zhang, P. Ji, A. Wang, J. Mei, A. Kortylewski, and A. Yuille, "3d-aware neural body fitting for occlusion robust 3d human pose estimation," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9399-9410, 2023.

[7] Z. Zhao, H. Yu, C. Lyu, P. Ji, X. Yang, and W. Yang, "Cross-Modal 2D-3D Localization with Single-Modal Query," in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 6171-6174, IEEE, July 2023.

[8] G. Lan, X. Y. Liu, Y. Zhang, and X. Wang, "Communication-efficient federated learning for resource-constrained edge devices," IEEE Transactions on Machine Learning in Communications and Networking, 2023.

[9] A. Zhu, K. Li, T. Wu, P. Zhao, W. Zhou, and B. Hong, "Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification," arXiv preprint arXiv:2404.14606, 2024.

[10] J. Jin, H. Xu, P. Ji, and B. Leng, "IMC-NET: Learning Implicit Field with Corner Attention Network for 3D Shape Reconstruction," in 2022 IEEE International Conference on Image Processing (ICIP), pp. 1591-1595, IEEE, October 2022.

[11] T. Xu, H. Yang, and T. Wu, "Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System," International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 12, no. 2, pp. 75-80, 2024. [Online]. Available: doi:10.55524/ijircst.2024.12.2.13

[12] S. Dlamini, Y. H. Chen, and C. F. J. Kuo, "Complete fully automatic detection, segmentation and 3D reconstruction of tumor volume for non-small cell lung cancer using YOLOv4 and region-based active contour model," Expert Systems with Applications, vol. 212, p. 118661, 2023.

[13] M. Xiao, Y. Li, X. Yan, M. Gao, and W. Wang, "Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example," arXiv preprint arXiv:2404.08279, 2024.

[14] A. Zhu, J. Li, and C. Lu, "Pseudo view representation learning for monocular RGB-D human pose and shape estimation," IEEE Signal Processing Letters, vol. 29, pp. 712-716, 2021.

[15] X. Yan, W. Wang, M. Xiao, Y. Li, and M. Gao, "Survival Prediction Across Diverse Cancer Types Using Neural Networks," arXiv preprint arXiv:2404.08713, 2024.

[16] X. S. Wang and B. P. Mann, "Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning," arXiv preprint arXiv:2010.01255, 2020.

[17] C. Szegedy et al., "Going deeper with convolutions," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, IEEE, Boston, 2015. doi:10.1109/CVPR.2015.7298594

[18] C. Wang, D. Chen, L. Hao, X. Liu, Y. Zeng, J. Chen, and G. Zhang, "Pulmonary image classification based on inception-v3 transfer learning model," IEEE Access, vol. 7, pp. 146533-146541, 2019.

[19] X. S. Wang, J. D. Turner, and B. P. Mann, "Constrained attractor selection using deep reinforcement learning," Journal of Vibration and Control, vol. 27, no. 5-6, pp. 502-514, 2021.

[20] Q. Ning et al., "Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning," Analytical and Bioanalytical Chemistry, vol. 414, no. 13, pp. 3959-3970, 2022.

[21] J. Yao, T. Wu, and X. Zhang, "Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn," arXiv preprint arXiv:2308.08333, 2023.

[22] G. Lan, H. Wang, J. Anderson, C. Brinton, and V. Aggarwal, "Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates," Advances in Neural Information Processing Systems, vol. 36, 2024.

[23] S. G. Armato III et al., "Data From LIDC-IDRI," The Cancer Imaging Archive. [Online]. Available: https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX, 2015.

[24] Y. Li, X. Yan, M. Xiao, W. Wang, and F. Zhang, "Investigation of Creating Accessibility Linked Data Based on Publicly Available Accessibility Datasets," in Proceedings of the 2023 13th International Conference on Communication and Network Security, pp. 77–81, Association for Computing Machinery, 2024.

[25] M. Souto, P. G. Tahoces, S. C. JJ, M. J. Lado, M. Remy-Jardin, J. Remy, and J. J. Vidal, "Automatic detection of pulmonary nodules on computed tomography: a preliminary study," Radiologia, vol. 50, no. 5, pp. 387-392, 2008.

[26] C. H. Lee and J. S. Jwo, "Automatic segmentation for pulmonary nodules in CT images based on multifractal analysis," IET Image Processing, vol. 14, no. 7, pp. 1347-1353, 2020.

Cites this article as

X. Fei, Y. Wang, L. Dai, M. S. , "Deep Learning-Based Lung Medical Image Recognition", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.3, pp.100-105, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.3.16

Corresponding Author

Xinghui Fei

Colorado State University, Fort Collins, USA

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