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Precise and automated lung cancer cell classification using deep neural network with multiscale features and model
Lan Tian1, Jiabao Wu1, Wanting Song1
1Department of Pulmonary and Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian, China.
Scientific Reports
|May 7, 2024
Summary
A new deep learning model accurately distinguishes between major lung cancer types, improving diagnostic precision. This advancement offers physicians enhanced support for critical clinical decisions in lung cancer treatment.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Lung cancer poses a significant global health challenge, with accurate differential diagnosis crucial for effective treatment and prognosis.
- Distinguishing between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma is critical for patient management.
Purpose of the Study:
- To develop an innovative deep learning model for precise and stable classification of major lung cancer subtypes.
- To enhance diagnostic accuracy in lung cancer through advanced image analysis techniques.
Main Methods:
- A deep learning model integrating Feature Pyramid Network (FPN), Squeeze-and-Excitation (SE) modules, and Residual Network (ResNet18) was developed.
- Knowledge distillation from larger models was employed to optimize the performance of compact student models.
- The model's efficacy was validated using five-fold cross-validation and ablation studies.
Main Results:
- The developed model achieved an average accuracy of 98.84% and a Matthews Correlation Coefficient (MCC) of 98.83%.
- The model demonstrated superior performance across all metrics compared to existing models.
- Ablation studies confirmed the performance enhancement contributed by each component of the model.
Conclusions:
- The deep learning model significantly improves the accuracy of lung cancer diagnosis.
- This provides physicians with more precise decision-making support for clinical practice.
- The study highlights the potential of advanced AI in improving oncological diagnostics.
