A Review of the Application of the ResNet18 Model in Medical and Industrial Visual Inspection
Research Article
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A Review of the Application of the ResNet18 Model in Medical and Industrial Visual Inspection

Zehou Liu 1*
1 School of Automation, Central South University, Changsha, China
*Corresponding author: 8207221825@csu.edu.cn
Published on 22 October 2025
Journal Cover
ACE Vol.196
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-451-9
ISBN (Online): 978-1-80590-452-6
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Abstract

This article systematically reviews the research status, key technological breakthroughs, and application prospects of the ResNet18 model and its improved variants in the fields of medical imaging and industrial vision detection. ResNet18 effectively alleviates the deep network degradation problem with its residual structure, providing an efficient benchmark model for tasks such as image classification, object detection, and defect recognition. This article focuses on analyzing the improvement strategies of ResNet18 for different application scenarios, such as introducing attention mechanisms (SE, SimAM), combining transfer learning, data augmentation, and model lightweight pruning, which significantly enhance performance in tasks including coronary artery narrowing classification, road pothole detection, metal defect evaluation, flame stability recognition, epilepsy EEG classification, and coal gangue sorting, achieving high accuracy levels generally ranging from 94% to 99.5%. Additionally, this article discusses challenges such as model lightweight deployment, few-shot learning, cross-domain generalization, and standardized dataset construction, and looks forward to future research directions, including deeper structural optimization, multimodal fusion, and embedded applications in broader industrial scenarios, providing comprehensive technical references for researchers in related fields.

Keywords:

ResNet18, deep learning, image recognition, attention mechanism, transfer learning, lightweight network

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Liu,Z. (2025). A Review of the Application of the ResNet18 Model in Medical and Industrial Visual Inspection. Applied and Computational Engineering,196,38-46.

References

[1]. X. Ye, Y. Jin, Z. Wang, N. Feng, L. Mu and G. Xie, "Image Binary Classification of Coronary Artery Stenosis Based on Resnet18 and Transfer Learning, " in 2023 35th Chinese Control and Decision Conference (CCDC), Yichang, China, 2023, pp. 2721-2726, doi: 10.1109/CCDC58219.2023.10326590.

[2]. R. Ma, M. Xu, Z. Li and X. Sun, "Classification of Pothole Laser Images Based on the Improved ResNet18 Network, " in 2024 3rd International Conference on Electronics and Information Technology (EIT), Chengdu, China, 2024, pp. 619-623, doi: 10.1109/EIT63098.2024.10762034.

[3]. S. She et al., "Evaluation of Defects Depth for Metal Sheets Using Four-Coil Excitation Array Eddy Current Sensor and Improved ResNet18 Network, " in IEEE Sensors Journal*, vol. 24, no. 12, pp. 18955-18967, 15 June15, 2024, doi: 10.1109/JSEN.2024.3367816.

[4]. Y. Guo, P. Liu, X. Mei and Z. Yang, "The Research on Galaxy Image Classification Model Based on ResNet18 and Transfer Learning, " 2024 8th International Workshop on Control Engineering and Advanced Algorithms (IWCEAA), Nanjing, China, 2024, pp. 139-142, doi: 10.1109/IWCEAA63616.2024.10823737.

[5]. N. Shahadat, "Lung Image Analysis using Squeeze and Excitation Based Convolutional Networks, " 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2023, pp. 1-6, doi: 10.1109/ICCIT60459.2023.10441501.

[6]. Z. Wang, Y. Bian, M. Yang and G. Liu, "Power Plant Furnace Flame Stability Detection Based on ResNet18-SimAM, " in 2024 IEEE Sustainable Power and Energy Conference (iSPEC), Kuching, Sarawak, Malaysia, 2024, pp. 321-325, doi: 10.1109/iSPEC59716.2024.10892397.

[7]. G. Xue, S. Li, P. Hou, S. Gao and R. Tan, "Study on a Resnet18-Based Lightweight Recognition ALgorithm of Coal and Gangue, " in 2024 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), Kusatsu, Japan, 2024, pp. 83-87, doi: 10.1109/IIKI65561.2024.00024.

[8]. Y. Yu, S. Yan and X. Hao, "Pedestrian Detection Based on Improved YOLOv3 Network, " 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT), Jilin, China, 2023, pp. 297-301, doi: 10.1109/ICCECT57938.2023.10141270. keywords: {Roads; Feature extraction; Real-time systems; Periodic structures; Pedestrian detection; YOLOv3; Convolutional Block Attention Module; SPP Structure},

[9]. V. K. Dubey, S. Sarkar, R. Shukla, G. Singh and A. Sahani, "Epileptic Seizure Stage Classification from EEG Signal Using ResNet18 Model and Data Augmentation, " in 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-5, doi: 10.1109/DELCON54057.2022.9753352.

References

[1]. X. Ye, Y. Jin, Z. Wang, N. Feng, L. Mu and G. Xie, "Image Binary Classification of Coronary Artery Stenosis Based on Resnet18 and Transfer Learning, " in 2023 35th Chinese Control and Decision Conference (CCDC), Yichang, China, 2023, pp. 2721-2726, doi: 10.1109/CCDC58219.2023.10326590.

[2]. R. Ma, M. Xu, Z. Li and X. Sun, "Classification of Pothole Laser Images Based on the Improved ResNet18 Network, " in 2024 3rd International Conference on Electronics and Information Technology (EIT), Chengdu, China, 2024, pp. 619-623, doi: 10.1109/EIT63098.2024.10762034.

[3]. S. She et al., "Evaluation of Defects Depth for Metal Sheets Using Four-Coil Excitation Array Eddy Current Sensor and Improved ResNet18 Network, " in IEEE Sensors Journal*, vol. 24, no. 12, pp. 18955-18967, 15 June15, 2024, doi: 10.1109/JSEN.2024.3367816.

[4]. Y. Guo, P. Liu, X. Mei and Z. Yang, "The Research on Galaxy Image Classification Model Based on ResNet18 and Transfer Learning, " 2024 8th International Workshop on Control Engineering and Advanced Algorithms (IWCEAA), Nanjing, China, 2024, pp. 139-142, doi: 10.1109/IWCEAA63616.2024.10823737.

[5]. N. Shahadat, "Lung Image Analysis using Squeeze and Excitation Based Convolutional Networks, " 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2023, pp. 1-6, doi: 10.1109/ICCIT60459.2023.10441501.

[6]. Z. Wang, Y. Bian, M. Yang and G. Liu, "Power Plant Furnace Flame Stability Detection Based on ResNet18-SimAM, " in 2024 IEEE Sustainable Power and Energy Conference (iSPEC), Kuching, Sarawak, Malaysia, 2024, pp. 321-325, doi: 10.1109/iSPEC59716.2024.10892397.

[7]. G. Xue, S. Li, P. Hou, S. Gao and R. Tan, "Study on a Resnet18-Based Lightweight Recognition ALgorithm of Coal and Gangue, " in 2024 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), Kusatsu, Japan, 2024, pp. 83-87, doi: 10.1109/IIKI65561.2024.00024.

[8]. Y. Yu, S. Yan and X. Hao, "Pedestrian Detection Based on Improved YOLOv3 Network, " 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT), Jilin, China, 2023, pp. 297-301, doi: 10.1109/ICCECT57938.2023.10141270. keywords: {Roads; Feature extraction; Real-time systems; Periodic structures; Pedestrian detection; YOLOv3; Convolutional Block Attention Module; SPP Structure},

[9]. V. K. Dubey, S. Sarkar, R. Shukla, G. Singh and A. Sahani, "Epileptic Seizure Stage Classification from EEG Signal Using ResNet18 Model and Data Augmentation, " in 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-5, doi: 10.1109/DELCON54057.2022.9753352.

Cite this article

Liu,Z. (2025). A Review of the Application of the ResNet18 Model in Medical and Industrial Visual Inspection. Applied and Computational Engineering,196,38-46.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.196
ISSN: 2755-2721(Print) / 2755-273X(Online)

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