Summary of A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-colour Space Feature Fusion and Quantum-classical Stack Ensemble Method, by Sambit Mallick et al.
A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
by Sambit Mallick, Snigdha Paul, Anindya Sen
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning approach combining color space ensembling and quantum-classical stacking is developed to improve breast cancer classification using histopathological images. The study employs the DenseNet121 architecture for feature extraction and various classifiers, including Random Forest, SVM, QSVC, and VQC. A unique feature fusion technique within the color space ensemble is introduced, which enhances the robustness of individual classifiers. The approach achieves high classification accuracy, nearing unity, when fusing different color spaces like RGB with HSV or RGB with CIE Luv. This research has implications for advancing diagnostic accuracy and treatment efficacy in medical diagnostics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Breast cancer diagnosis can be improved by using special images and combining different ways of classifying data. Researchers developed a new method that combines two approaches: color space ensembling and quantum-classical stacking. They used special features from images and various methods to classify them, like Random Forest or SVM. The new approach is more accurate than before and could help doctors diagnose breast cancer better. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Machine learning » Random forest
