An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer
Abstract
:1. Introduction
- i.
- A fully automated framework for the five-class diagnosis of most occurring lung and colon cancer subtypes is proposed using EffcientNetV2-large (L), medium (M), and small (S) models based on histopathology images.
- ii.
- These existing pretrained models are finetuned and tested using a large, openly available lung and colon cancer histopathology image dataset called LC25000.
- iii.
- Visual saliency maps are provided using the gradCAM method to understand the model decisions during testing better.
Related Work
Study | Year | Method | Dataset | Interpretability | Performance (%) |
---|---|---|---|---|---|
Chehade A. H. et al. [25] | 2022 | ML classifiers | LC25000 | No | Accuracy: 99.0 F1-score: 98.80 |
Masud M. et al. [24] | 2021 | ML classifiers | LC25000 | No | Accuracy: 96.33 |
Ali M. et al. [26] | 2021 | Multi-input capsule neural network | LC25000 | No | Accuracy: 99.58 |
Togacar M. [28] | 2021 | DarkNet-19 and SVM | LC25000 | No | Accuracy: 99.69 |
Mehmood S. et al. [27] | 2022 | Image enhancement and AlexNet | LC25000 | No | Accuracy: 98.40 |
Teramoto A. et al. [21] | 2017 | Custom CNN model | Private dataset (298 microscopic images) | No | Accuracy: 71.10 (Only lung cancer) |
Attallah O. et al. [31] | 2022 | Custom CNN + PCA, FWHT, DWT | LC25000 | No | Accuracy: 99.60 |
Hatuwal B. K. et al. [22] | 2020 | Custom CNN | LC25000 | No | Accuracy: 97.20 (Only lung cancer) |
Mangal S. et al. [32] | 2020 | Custom CNN | LC25000 | No | Accuracy: 96.50 |
Talukder Md. A. et al. [30] | 2022 | Hybrid ensemble learning | LC25000 | No | Accuracy: 99.30 |
Kumar N. et al. [29] | 2022 | DenseNet121 and RF | LC25000 | No | Accuracy: 98.60 F1-score: 98.50 |
Hasan Md. I. et al. [23] | 2022 | Custom CNN and PCA | LC25000 | No | Accuracy: 99.80 (Only colon cancer) |
Present study | 2023 | EffcientNetV2 | LC25000 | Yes | Accuracy: 99.97 F1-score: 99.97 BA: 99.97 AUC: 99.99 MCC: 99.96 |
2. Methods
2.1. Dataset
2.2. Physiological Mechanims of Lung and Colon Cancers
2.3. EffcientNetV2 and Compound Scaling
2.4. Model Training and Validation
2.5. Visual Saliency Maps
2.6. Evaluation Metrics
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Lung-aca | Lung-n | Lung-scc | Colon-aca | Colon-n | |
---|---|---|---|---|---|
Training | 3600 | 3600 | 3600 | 3600 | 3600 |
Validation | 400 | 400 | 400 | 400 | 400 |
Testing | 1000 | 1000 | 1000 | 1000 | 1000 |
EffcientNetV2-S | EffcientNetV2-M | EffcientNetV2-L | |
---|---|---|---|
Accuracy | 99.90 | 99.96 | 99.97 |
AUC | 99.99 | 99.99 | 99.99 |
F1-Score | 99.90 | 99.96 | 99.97 |
BA | 99.90 | 99.97 | 99.97 |
MCC | 99.87 | 99.94 | 99.96 |
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Tummala, S.; Kadry, S.; Nadeem, A.; Rauf, H.T.; Gul, N. An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer. Diagnostics 2023, 13, 1594. https://doi.org/10.3390/diagnostics13091594
Tummala S, Kadry S, Nadeem A, Rauf HT, Gul N. An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer. Diagnostics. 2023; 13(9):1594. https://doi.org/10.3390/diagnostics13091594
Chicago/Turabian StyleTummala, Sudhakar, Seifedine Kadry, Ahmed Nadeem, Hafiz Tayyab Rauf, and Nadia Gul. 2023. "An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer" Diagnostics 13, no. 9: 1594. https://doi.org/10.3390/diagnostics13091594