Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methodology
2.1. Data Collection and Preprocessing
- The training dataset (71% of total images, n = 662 images) comprised 401 cancerous and 261 healthy images.
- This was split further to training and validation datasets with 90% (n = 596 images) and 10% (n = 66 images). The training dataset was used to train the model’s parameters. In contrast, the validation dataset was used to fine-tune the model and optimize hyperparameters. We followed National Cancer Institute criteria to differentiate between normal and cancer cells [7].
- The test dataset (29% of total images, n = 272) included 156 cancerous and 116 healthy images. It was employed to assess the model’s final performance and generalization capability.
2.2. Model Selection
2.3. Separable Convolutional Neural Networks
2.4. Residual Blocks
2.5. Model Architecture
2.6. Model Optimization and Training
2.7. Statistical Analysis
3. Results
3.1. Accuracy and Validation
3.2. Model Application
3.3. Comparison of the Model Results with the Pathologist’s Decision
4. Discussion
4.1. Main Findings
4.2. Comparison with Existing ML Models in Lung Cancer Detection
4.3. Clinical Implications
4.4. Model Limitations
4.5. Ethical Considerations and Potential Social Impacts
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Dataset | Accuracy | AUC |
---|---|---|
Training dataset | 98% | 99% |
Validation dataset | 96% | 97% |
Testing dataset | 97% | 98% |
Image nr. | Model | Pathologist Decision (n = 10) | Pathologist Slide Accuracy (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Decision | Array | Accuracy(%) | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 | P10 | ||
1 | H | <0.001 | 100 | H | 100 | |||||||||
2 | <0.001 | 100 | 100 | |||||||||||
3 | <0.001 | 100 | 100 | |||||||||||
4 | 0.05 | 100 | 100 | |||||||||||
5 | 0.004 | 100 | 100 | |||||||||||
6 | <0.001 | 100 | H | C | 90 | |||||||||
7 | C | 1 | 100 | UC | 0 | |||||||||
8 | 1 | 100 | UC | C | UC | C | UC | 20 | ||||||
9 | 1 | 100 | C | 100 | ||||||||||
10 | 1 | 100 | 100 | |||||||||||
11 | 1 | 100 | C | UC | 90 | |||||||||
12 | 1 | 100 | UC | C | 70 | |||||||||
13 | 1 | 100 | C | UC | C | UC | C | UC | C | 40 | ||||
14 | 1 | 100 | C | 100 | ||||||||||
15 | 1 | 100 | UC | C | 80 | |||||||||
Pathologist year of experience | 11 | 11 | 14 | 12 | 12 | 14 | 12 | 11 | 13 | 10 | ||||
Individual pathologist accuracy (%) | 73.33 | 73.33 | 93.33 | 80.00 | 80.00 | 93.33 | 80.00 | 73.33 | 86.67 | 66.67 |
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Ahmed, A.A.; Fawi, M.; Brychcy, A.; Abouzid, M.; Witt, M.; Kaczmarek, E. Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis. Cancers 2024, 16, 1506. https://doi.org/10.3390/cancers16081506
Ahmed AA, Fawi M, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis. Cancers. 2024; 16(8):1506. https://doi.org/10.3390/cancers16081506
Chicago/Turabian StyleAhmed, Alhassan Ali, Muhammad Fawi, Agnieszka Brychcy, Mohamed Abouzid, Martin Witt, and Elżbieta Kaczmarek. 2024. "Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis" Cancers 16, no. 8: 1506. https://doi.org/10.3390/cancers16081506
APA StyleAhmed, A. A., Fawi, M., Brychcy, A., Abouzid, M., Witt, M., & Kaczmarek, E. (2024). Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis. Cancers, 16(8), 1506. https://doi.org/10.3390/cancers16081506