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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial

Lubna Abdelkareim Gabralla¹, Ali Mohamed Hussien², Abdulaziz AlMohimeed³

¹Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

A new deep learning model accurately predicts colon cancer by stacking convolutional neural network (CNN) models. This advanced technique improves early detection and treatment outcomes for this common disease.

Keywords:

CNN colon cancer explainable AI (XAI)stacking ensemble transfer learning

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Area of Science:

Oncology
Computer Science
Artificial Intelligence

Background:

Colon cancer is a leading global cancer, with nearly two million cases in 2020.
Accurate early detection is crucial for successful colon cancer treatment.
Deep learning offers potential for enhanced diagnostic accuracy in medical imaging.

Purpose of the Study:

To propose a novel heterogenic stacking deep learning model for colon cancer prediction.
To improve the performance of colon cancer detection using integrated deep learning approaches.
To evaluate the proposed model against established deep learning architectures.

Main Methods:

Developed a heterogenic stacking deep learning model integrating pretrained convolutional neural network (CNN) models.
Utilized a metalearner to enhance prediction performance within the stacking framework.
Evaluated the model on the LC25000 and WCE colon cancer image datasets (binary and multiclassified).
Compared performance against VGG16, InceptionV3, Resnet50, and DenseNet121 using accuracy, recall, precision, and F1 score.

Main Results:

The proposed stacking deep learning model achieved superior performance on both datasets.
For LC25000, the stacked model attained 100% accuracy, recall, precision, and F1 score.
For WCE, the stacked model achieved 98% accuracy, recall, precision, and F1 score.
Stacking-SVM demonstrated higher performance than individual models like VGG16, InceptionV3, Resnet50, and DenseNet121.

Conclusions:

Heterogenic stacking deep learning models significantly enhance colon cancer prediction accuracy.
The proposed stacking approach offers a robust and highly effective method for colon cancer detection.
Explainable AI (XAI) methods can be applied to understand black-box deep learning models in this context.