Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System
Keywords:
Deep Learning, Target Detection, Neural Network, Cancer Cell Detection, YOLOv5Abstract
As artificial intelligence (AI) theory becomes more sophisticated and its utilization spreads across daily life, education, and professional settings, the adoption of AI for medical diagnostic and service purposes stands as a logical progression in the evolution of medical technologies. This document outlines a novel approach to detecting cancer cell targets using a deep learning-based system, marking a critical step towards integrating AI into cancer diagnostics. The process of detecting cancer cell targets entails the localization of cell types within images of cells. By capitalizing on the strengths of the YOLOv5 model—a deep learning-driven, end-to-end, real-time object detection framework known for its efficiency, superior performance, adaptability, and user-friendly PyTorch integration—this research presents an enhanced YOLOv5 model incorporating both a feature pyramid network and the original YOLOv5 architecture. The ultimate aim is to facilitate precise detection of targets in cancer cell images. The experimental data demonstrate the system's negligible error rate in detection, swift processing capabilities, and exceptional reliability.
References
S. Diao et al., "Deep multi-magnification similarity learning for histopathological image classification," IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 3, pp. 1535-1545, 2023.
W. Huang, A. Rezvani, H. Chen, Y. Ni, S. Yun, S. Jeong, and M. Imani, "A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission," arXiv preprint arXiv:2402.02043, 2024.
S. Yun, H. Chen, R. Masukawa, H. E. Barkam, A. Ding, W. Huang, et al., "HyperSense: Accelerating Hyper-Dimensional Computing for Intelligent Sensor Data Processing," arXiv preprint arXiv:2401.10267, 2024.
H. Chen, W. Huang, Y. Ni, S. Yun, F. Wen, H. Latapie, and M. Imani, "TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection," arXiv preprint arXiv:2403.08108, 2024.
Z. Deng, Y. Xin, X. Qiu, and Y. Chen, "Weakly and semi-supervised deep level set network for automated skin lesion segmentation," in Innovation in Medicine and Healthcare: Proceedings of 8th KES-InMed 2020, pp. 145-155, Springer Singapore, 2020.
S. Diao, Y. Tian, W. Hu, J. Hou, R. Lambo, Z. Zhang, et al., "Weakly supervised framework for cancer region detection of hepatocellular carcinoma in whole-slide pathologic images based on multiscale attention convolutional neural network," The American journal of pathology, vol. 192, no. 3, pp. 553-563, 2022.
Z. Qi, J. Wang, X. Wu, and H. Zhao, "OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection," arXiv preprint arXiv:2306.01738, 2023.
Z. Li, H. Zhu, H. Liu, J. Song, and Q. Cheng, "Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection," arXiv preprint arXiv:2403.02232, 2024.
S. Li, P. Kou, M. Ma, H. Yang, S. Huang, and Z. Yang, "Application of Semi-supervised Learning in Image Classification: Research on Fusion of Labeled and Unlabeled Data," IEEE Access, 2024.
Y. Xin, S. Luo, P. Jin, Y. Du, and C. Wang, "Self-Training with Label-Feature-Consistency for Domain Adaptation," in International Conference on Database Systems for Advanced Applications, pp. 84-99, Cham: Springer Nature Switzerland, 2023, April.
H. Xu and J. A. Colmenares, "Admission Control with Response Time Objectives for Low-latency Online Data Systems," arXiv preprint arXiv:2312.15123, 2023.
X. Yan, M. Xiao, W. Wang, Y. Li, and F. Zhang, "A Self-Guided Deep Learning Technique for MRI Image Noise Reduction," Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, pp. 109–117, 2024. [Online]. Available: https://doi.org/10.53469/jtpes.2024.04(01).15
L. Li, "CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 513-522, 2024.
W. Huang, M. Bulut, R. van Lieshout, and K. Dellimore, "Exploration of using a pressure sensitive mat for respiration rate and heart rate estimation," in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 298-301, IEEE, November 2021.
Y. Xin, J. Du, Q. Wang, Z. Lin, and K. Yan, "VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding," arXiv preprint arXiv:2312.08733, 2023.
J. Chen, X. Chen, R. Wang, C. Le, A. Khalilian-Gourtani, E. Jensen, et al., "Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals," bioRxiv, 2024-03.
H. Xu, S. Wen, A. Gimenez, T. Gamblin, and X. Liu, "DR-BW: identifying bandwidth contention in NUMA architectures with supervised learning," in 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 367-376, IEEE, May 2017.
Y. Liu, H. Yang, and C. Wu, "Unveiling patterns: A study on semi-supervised classification of strip surface defects," IEEE Access, vol. 11, pp. 119933-119946, 2023.
Q. He, H. Su, J. Zhang, and X. Hou, "Frustratingly easy regularization on representation can boost deep reinforcement learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20215-20225, 2023.
K. He et al., "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, June 2016, pp. 770-778. [Online]. Available: https://doi.org/10.1109/CVPR.2016.90
X. Chen, R. Wang, A. Khalilian-Gourtani, L. Yu, P. Dugan, D. Friedman, et al., "A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis," bioRxiv, 2023-09.
C. Liu, J. Cui, R. Shang, Y. Xiao, Q. Jia, and E. Gehringer, "Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning," International Educational Data Mining Society, 2022.
J. Y. Shi et al., "Combining incremental conductance and firefly algorithm for tracking the global MPP of PV arrays," Journal of Renewable and Sustainable Energy, vol. 9, no. 2, 2017.
S. Chen, L. Lu, Q. Zhang, and M. Li, "Optimal binomial reliability demonstration tests design under acceptance decision uncertainty," Quality Engineering, vol. 32, no. 3, pp. 492-508, 2020.
Z. Qi, J. Wang, X. Wu, and H. Zhao, "OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection," arXiv preprint arXiv:2306.01738, 2023.
Y. Xiao et al., "Modeling review helpfulness with augmented transformer neural networks," in 2022 IEEE 16th International Conference on Semantic Computing (ICSC), pp. 83-90, January 2022.
R. Wang et al., "Distributed feedforward and feedback cortical processing supports human speech production," Proceedings of the National Academy of Sciences, vol. 120, no. 42, article e2300255120, 2023.
D. Ma et al., "Implementation of computer vision technology based on artificial intelligence for medical image analysis," International Journal of Computer Science and Information Technology, vol. 1, no. 1, pp. 69-76, 2023..
Zang, "Precision Calibration of Industrial 3D Scanners: An AI-Enhanced Approach for Improved Measurement Accuracy," Global Academic Frontiers, vol. 2, no. 1, pp. 27-37, 2024. [Online]. Available: https://gafj.org/journal/article/view/30
W. Wang et al., "Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 1, pp. 26–34, 2024. [Online]. Available: https://ijircst.irpublications.org/index.php/ijircst/article/view/21
R. Wang et al., "Stimulus speech decoding from human cortex with generative adversarial network transfer learning," in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 390-394, April 2020.
J. Wu et al., "SwitchTab: Switched Autoencoders Are Effective Tabular Learners," arXiv preprint arXiv:2401.02013, 2024.
W. Dai et al., "Addressing Unintended Bias in Toxicity Detection: An LSTM and Attention-Based Approach," in 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 375-379, November 2023.
R. Yuan, S. Ding, and C. Wang, "Comparative Analysis of Machine-Learning Models for Recognizing Lane-Change Intention Using Vehicle Trajectory Data," Infrastructures, vol. 8, no. 11, p. 156, 2023.
Y. Xin et al., "Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey," arXiv preprint arXiv:2402.02242, 2024.
Q. Jia et al., "All-in-one: Multi-task learning BERT models for evaluating peer assessments," arXiv, 2021.
H. Xu, S. Song, and Z. Mao, "Characterizing the Performance of Emerging Deep Learning, Graph, and High-Performance Computing Workloads Under Interference," arXiv:2303.15763, 2023.
F. Liu, H. Wang, W. Chen, H. Sun, and Y. Duan, "Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation," arXiv preprint arXiv:2403.09625, 2024.
S. Chen, J. Wu, N. Hovakimyan, and H. Yao, "Recontab: Regularized contrastive representation learning for tabular data," arXiv preprint arXiv:2310.18541, 2023.
Y. Xin, J. Du, Q. Wang, K. Yan, and S. Ding, "MmAP: Multi-modal Alignment Prompt for Cross-domain Multi-task Learning," arXiv preprint arXiv:2312.08636, 2023.
T. Zhang, L. Li, C. Igel, S. Oehmcke, F. Gieseke, and Z. Peng, "LR-CSNet: low-rank deep unfolding network for image compressive sensing," in 2022 IEEE 8th International Conference on Computer and Communications (ICCC), pp. 1951-1957, IEEE, Dec. 2022.
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.
N. S. Lesmana, H. Su, and C. S. Pun, "Reinventing Policy Iteration under Time Inconsistency," Transactions on Machine Learning Research, 2022.
F. Ni, H. Zang, and Y. Qiao, "Smartfix: Leveraging machine learning for proactive equipment maintenance in industry 4.0," in The 2nd International scientific and practical conference “Innovations in education: prospects and challenges of today”(January 16-19, 2024), Sofia, Bulgaria, International Science Group, 2024, pp. 313.
L. Li, "Hierarchical Edge Aware Learning for 3D Point Cloud," in Computer Graphics International Conference, pp. 81-92, Cham: Springer Nature Switzerland, Aug. 2023.
X. S. Wang and B. P. Mann, "Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning," arXiv preprint arXiv:2010.01255, 2020.
