Artificial Intelligence based breast thermography using radiomic feature extraction versus conventional manual interpretation of breast thermograms in the prediction of breast cancer: a multi-reader study

Among women, breast cancer is one of the most commonly occurring cancers besides skin and cervix cancer. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and treatment. Thermography-based technology, aided with machine learning, for screening/diagnosing breast cancer is non-invasive, cost-wise appropriate, and requires very little equipment in rural areas with limited facilities. In this paper, we systematically compare the state-of-the-art feature extraction approaches on a uniform platform, using two Common datasets, three Feature Selection methods, four well-known Classifiers, and three Cross-Validation strategies and analyze the results, for a fair comparison. Also, we evaluated the performance when all the features were combined (Unified Model) on the same platform. Experimental results show that the classification accuracy improves considerably with the use of feature selection methods. Among all the combinations considered, the classification model with Union_FeatureSet and mRMR gave the best performance for both datasets. We obtained a feature subset of 26 and 34 features (from Union_FeatureSet) with a combination of mRMR and SVM, which are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 95.73% on the DMR-IR dataset and 92.533% on the RGC-IR dataset.

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Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets

Rezayiye,

Ibarra-Castanedo,

Maldague

2024

Algorithms

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This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications.

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Artificial Intelligence based breast thermography using radiomic feature extraction versus conventional manual interpretation of breast thermograms in the prediction of breast cancer: a multi-reader study

Cited by 3 publications

References 42 publications

Radiomics Feature Selection from Thyroid Thermal Images to Improve Thyroid Nodules Interpretations

Radiomics Feature Selection from Thyroid Thermal Images to Improve Thyroid Nodules Interpretations

Performance Evaluation of Thermography-Based Computer-Aided Diagnostic Systems for Detecting Breast Cancer: An Empirical Study

Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets

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