Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification

Abstract

Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. However, diagnosis, which is mostly dependent on histopathologists’ competence, presents difficulties and hazards when expertise is insufficient. While diagnostic methods like imaging and blood markers contribute to early detection, histopathology remains the gold standard, although timeconsuming and vulnerable to inter-observer mistakes. Limited access to high-end technology further limits patients’ ability to receive immediate medical care and diagnosis. Recent advances in deep learning have generated interest in its application to medical imaging analysis, specifically the use of histopathological images to diagnose lung and colon cancer. The goal of this investigation is to use and adapt existing pre-trained CNN-based models, such as Xception, DenseNet201, ResNet101, InceptionV3, DenseNet121, DenseNet169, ResNet152, and InceptionResNetV2, to enhance classification through better augmentation strategies. The results show tremendous progress, with all eight models reaching impressive accuracy ranging from 97% to 99%. Furthermore, attention visualization techniques such as GradCAM, GradCAM++, ScoreCAM, Faster Score-CAM, and LayerCAM, as well as Vanilla Saliency and SmoothGrad, are used to provide insights into the models’ classification decisions, thereby improving interpretability and understanding of malignant and benign image classification. Our research implementations are open to the public at: https://github.com/Mukaffi28/Explainable-AI-for-Lung-and-Colon-Cancer-Classification

Table of Contents

• Proposed Methodology
• Dataset Availability
• Results
• Contact Information
• Citation

Proposed Methodology

Dataset Availability

We utilized the LC25000 dataset.

The dataset can be accessed from here.

Results

Performance Metrics Comparison of Various Pre-trained CNN Models

Model	Accuracy	Precision	Recall	F1-Score	Jaccard Score	Log Loss
Xception	0.9989	0.9989	0.9989	0.9989	0.9978	0.0384
DenseNet201	0.9971	0.9971	0.9971	0.9971	0.9942	0.1057
ResNet101	0.9928	0.9928	0.9928	0.9928	0.9858	0.2595
InceptionV3	0.9904	0.9907	0.9904	0.9904	0.9812	0.3460
DenseNet121	0.9896	0.9898	0.9896	0.9896	0.9795	0.3749
DenseNet169	0.9888	0.9888	0.9888	0.9888	0.9781	0.4037
ResNet152	0.9885	0.9886	0.9885	0.9885	0.9774	0.4133
InceptionResNetV2	0.9765	0.9765	0.9765	0.9765	0.9547	0.8458

Confusion matrix

Contact Information

For any questions, collaboration opportunities, or further inquiries, please feel free to reach out:

• Mukaffi Bin Moin

  • Email: [email protected]

• Fatema Tuj Johora Faria

  • Email: [email protected]

• Swarnajit Saha

  • Email: [email protected]

• Bushra Kamal Rafa

  • Email: [email protected]

Citation

If you find the associated research work helpful, please consider citing our paper:

@misc{moin2024exploringexplainableaitechniques,
      title={Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification}, 
      author={Mukaffi Bin Moin and Fatema Tuj Johora Faria and Swarnajit Saha and Busra Kamal Rafa and Mohammad Shafiul Alam},
      year={2024},
      eprint={2405.04610},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2405.04610}, 
}