Thermofusionnet for breast abnormality detection through visual and infrared thermal imaging using deep learning

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This model employs deep learning for accurate detection of breast cancer, suggesting new diagnostic methods.

The ThermoFusionNet model improves breast cancer detection accuracy compared to traditional methods.
Experimental results indicate that malignant cases exhibit distinct thermal patterns, aiding reliable detection.
Integration of visual and infrared thermal imaging enhances the sensitivity and specificity of abnormality detection.
The study proposes adaptive filtering methods to reduce noise while preserving important image details.

Ghatge et al. (2025) studied this question.

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View Full Paper

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Authors

This model employs deep learning for accurate detection of breast cancer, suggesting new diagnostic methods.

The ThermoFusionNet model improves breast cancer detection accuracy compared to traditional methods.
Experimental results indicate that malignant cases exhibit distinct thermal patterns, aiding reliable detection.
Integration of visual and infrared thermal imaging enhances the sensitivity and specificity of abnormality detection.
The study proposes adaptive filtering methods to reduce noise while preserving important image details.

Ghatge et al. (2025) studied this question.

View Full Paper

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Bookmark