Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis

Area of Science:

• Computational pathology
• Medical image analysis
• Artificial intelligence in oncology

Background:

• Histopathology is crucial for accurate cancer diagnosis and identifying prognostic/therapeutic targets.
• Early cancer detection significantly improves patient survival rates.
• Deep learning shows promise in analyzing cancer histopathology, particularly for lung and colon cancers.

Purpose of the Study:

• To evaluate the efficacy of deep learning networks in diagnosing cancers using histopathology images.
• To develop and assess a novel fine-tuned deep network for improved processing of colon and lung cancer histopathology images.
• To enhance the performance of deep learning architectures through specific optimization techniques.

Main Methods:

• A novel fine-tuning approach was developed for a deep learning network.
• Techniques including regularization, batch normalization, and hyperparameter optimization were employed.
• The proposed model was evaluated on the LC2500 dataset for colon and lung cancer diagnosis.

Main Results:

• The fine-tuned model achieved high performance metrics: 99.84% precision, 99.85% recall, 99.84% F1-score, 99.96% specificity, and 99.94% accuracy.
• The model, based on the pre-trained ResNet101 network, demonstrated superior performance compared to state-of-the-art methods.
• Experimental findings confirm the effectiveness of the proposed fine-tuned learning model.

Conclusions:

• The developed fine-tuned deep learning model offers a significant advancement in histopathology-based cancer diagnosis.
• This approach shows potential for improving early detection rates and patient outcomes for lung and colon cancers.
• The study highlights the effectiveness of tailored deep learning strategies in medical image analysis.