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Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining.
Ling Yang1, Shengguang Peng2, Rebaz Othman Yahya3
1School of Informatics, Harbin Guangsha College, Harbin, 150025, Heilongjiang, China.
Journal of Cancer Research and Clinical Oncology
|July 24, 2023
Summary
This study demonstrates that combining deep complex neural networks with data mining significantly enhances breast cancer diagnosis accuracy and speed. The hybrid approach effectively analyzes thermographic images for improved detection of cancerous cells.
Area of Science:
- Medical imaging
- Artificial intelligence in healthcare
- Computational biology
Background:
- Accurate and timely breast cancer diagnosis is crucial.
- Deep complex neural networks (DNNs) and data mining offer advanced diagnostic capabilities.
- Hybrid approaches can improve diagnostic accuracy and speed.
Purpose of the Study:
- To evaluate a hybrid approach combining deep complex neural networks, data mining, and thermography for breast cancer diagnosis.
- To assess the effectiveness of this integrated method in improving diagnostic accuracy and speed.
Main Methods:
- Utilized data mining to extract key features differentiating healthy and cancerous cells.
- Employed deep complex neural networks (ResNet18, ResNet50, VGG19, Xception) for analyzing thermographic images.
- Collected 1870 thermographic images from 187 volunteers (152 healthy, 35 cancer patients).
Main Results:
- Thermography provides a safe, cost-effective breast imaging method.
- Deep complex neural network models were trained to classify benign and malignant thermal images.
- The study analyzed 1870 thermographic images from 187 participants.
Conclusions:
- A combined approach using deep complex neural networks and data mining significantly improves breast cancer diagnosis.
- This hybrid method enhances both the accuracy and speed of detecting cancerous cells.
- The findings highlight the potential of AI and data mining in medical diagnostics.
