Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches
Abstract
1. Introduction
- A comprehensive review of recent advancements in texture analysis techniques and machine learning approaches specifically applied to breast cancer detection using infrared thermography, filling a gap in previous reviews that did not adequately emphasize texture analysis, and showing that this approach achieves top performance.
- A systematic analysis of the complete infrared thermography processing pipeline, including image preprocessing techniques, feature extraction methods, feature reduction techniques, classification approaches, and performance assessment metrics used in thermographic breast cancer detection, rather than focusing on isolated components of the workflow.
- A critical analysis of the current limitations in infrared thermography research, particularly noting the over-reliance on limited thermal image datasets (primarily from the DMR-IR database). This study also notes that some reported results may be unreliable due to potential leakage caused by splitting patient images between training and test sets which could contribute to model overfitting.
- The identification of promising research directions, highlighting how automated analysis through texture analysis and machine learning can address practical implementation challenges, such as the shortage of specialized radiologists and differing interpretation standards, bridging technical advances and clinical application. Numerous approaches achieved very high performance; therefore, this review advo-cates investing in research focused on developing tools that improve radiologists’ comprehension of medical images and the rationale behind CAD system recommendations.
2. Computer-Aided Diagnosis System Architecture
3. Computer-Aided Diagnosis for Breast Cancer Detection
3.1. Image Acquisition
Datasets
- DMR-IR Dataset: The Database for Mastology Research Infrared (DMR-IR) dataset [39] is the most widely used database in research studies. Of the 26 studies covered in this review, 20, 77%, used this dataset. The DMR-IR dataset includes infrared (IR) images, several digitized mammograms, several ROI masks, and clinical data for 293 patients captured at the Hospital Universitario Antonio Pedro (HUAP) of the Federal University Fluminense. The use of this dataset was approved by the Ethical Committee of the HUAP and registered with the Brazilian Ministry of Health under number CAAE: 01042812.0.0000.5243 and is publicly available at http://visual.ic.uff.br/dmi/, accessed on 6 April 2025. Infrared images are captured using Static Image Thermography (SIT) and Dynamic Image Thermography (DIT) described in [19]. The database also includes segmented images for 56 patients (37 sick and 19 normal). Figure 4 shows sample images from this dataset.
3.2. Image Preprocessing
3.3. Feature Extraction
3.3.1. Statistical Methods
- First Order Statistics (FOS): This depends on individual pixel values and not on their interaction with other pixels. Captured statistics include entropy, energy, maximum, minimum, inter-quantile range, mean, standard deviation, mean absolute deviation, variance, range, root mean square, skewness, uniformity, and kurtosis [74,79]. Entropy measures the average level of information in an image, and therefore, breasts without cancer should have a lower entropy due to their homogenous temperature distribution, while a cancerous breast would have a higher entropy due to vascularization. Skewness measures distribution asymmetry, and therefore, a breast with cancer should show a higher skewness due to greater temperature values. See Table A1 in Appendix A for the first-order statistic equations.
- Tamura: These are features that globally quantify six texture characteristics: coarseness, contrast, directionality, line-likeness, roughness, and regularity [80]. The equations for calculating these six features are in Table A2 in Appendix A.
- Co-occurrence Matrix: This was developed by Haralick et al. [78] to codify textural information by calculating second-order statistics on the spatial relationships of gray tones in an image. This spatial relationship is captured in a matrix, called a Gray-level co-occurrence matrix (GLCM) of size . Let g(a,b) represent an entry in the matrix that records the number of pixel pairs in image I that are separated by a specified angle and distance, where one pixel has a gray level of a and the other has a gray level of b. Figure 6 shows the neighboring pixels for all angles of distance 1.
- Non-parametric local transforms: rely on the relative ordering of local pixels, not on their intensity value. It includes Census Transform (CT) [86], Local Ternary Pattern (LTP) [76], Local Directional Number Pattern (LDN) [89], and local binary pattern (LBP) [87], which encode the local textural and structural properties of an image as binary codes.
3.3.2. Model-Based Methods
Citation | Method | Features | Advantages | Limitations |
---|---|---|---|---|
[41,47,50,52] | Fractal [106] | Captures texture self-similarity patterns in the image. Includes fractal dimensions [91,107], Hurst exponent [92], and lacunarity [108]. | Blood vessel growth is fractal [109]. Captures self-similar natural patterns. Invariant to rotation. Robust to illumination variations. | Inability to detect non-fractal patterns. Interpretability challenges. Sensitive to scale and noise. |
[47,49,51] | Vascular Network | Models blood vessel development as a network. | Captures blood vessel network. | Accuracy of model. May require manual tuning. |
- Fractal: This represents texture as a self-similar pattern under varying degrees of magnification [110]. It was introduced into image processing by Pentland [106] and has been widely applied across image analysis, especially in medical image analysis [111]. Furthermore, many natural phenomena are fractal, including blood vessel growth and flow [109]. Therefore, fractal textures may prove effective in detecting cancer lesions in thermographic images.
- Vascular Network: Several studies developed models to capture the vasodilation and angiogenesis of blood vessels. During preprocessing, Pramanik et al. [51] applied a breast blood profusion model based on breast thermal physiology which they developed and published in [26]. Chebbah et al. [49] applied thresholding and morphological operations (medial axis transformation) and a skeletonization algorithm called homotopic thinning to yield features representing blood vessels. Abdel-Nasser et al. [47] applied lacunarity analysis of Vascular Networks [120], but HOG outperformed it.
3.3.3. Signal Processing Methods
Citation | Method | Features | Advantages | Limitations |
---|---|---|---|---|
[46,122] | Spatial Domain Filters [74] | Obtain pixel value by applying operation to pixel neighbor. Used for edge detection and feature extraction. Includes Sobel [123], Canny [124] and HED [125]. | Capture fine textures and edges. | Sensitive to noise. Does not capture course details. |
[46] | Gabor Filter [126] | Captures spatial frequency texture information. Multi-scale and multi-orientation. | Capture course and fine detail. Spatial localization. Robust to illumination variations. | Sensitive to noise. Requires parameter tuning. Not rotational invariant. High dimensional vector. |
[31,32,48,127] | Wavelet Analysis [90] | Represent textures spatially and frequency at multiple scales. | Capture course and fine detail. Spatial localization. Robust to illumination variations and noise. | Requires parameter tuning. Not rotational invariant. High dimensional vector. |
[48] | Curvelet Transform [128] | Decomposes images into small, elongated wave-like shapes that capture details at different scales and orientations. | Identify vascular structures. Multi-scale and multi-orientation. Detection of curved edges. | Not in standard libraries. Not rotational invariant. |
[29,44,45,46,47,127] | HOG [33] | Splits image into cells, calculates gradient/pixel and builds histograms of gradients/cells. Normalizes gradient in region of cells. Cells retain spatial detail. | Cells capture spatial detail. Identify shapes with distinct edges. Robust to illumination and geometric changes. Robust to noise and cluttered background. Detects abnormal structures in medical images. | Reliant on strong edge features. Dependent on manual choice of parameters. Output is a high-dimensional feature vector. |
- Spatial Domain Filters: Edge detection was employed by a few studies. Dihmani et al. [46] tested and compared the Canny edge detector [124] against HOG, LBP, and Gabor filters, but HOG achieved the best result. Youssef et al. [29] enhanced a thermographic with edges generated by the Canny and Holistically nested edge detector (HED) [125]. The HED is an end-to-end edge and boundary detector based on CNN.
- Gabor Filter: This is a linear filter that identifies frequencies in a point’s localized area in a specified direction and is represented as a 2D Gaussian kernel modulated by a sinusoidal function [74]. The formula for the Gabor filter is as follows:
- Wavelet Analysis: Wavelets [90] are filters that decompose a signal in both space and time across a scale hierarchy. de Santana et al. [31] extract features using the Deep-Wavelet Neural Network (DWNN) based on the Haar Discrete Transform of Wavelets. They used 336 frontal images from the HC-UFPE dataset, which classifies an image as cyst, benign lesion, malignant lesion, or no lesion. The images were converted to grayscale and fed into DWNN and then classified with various classifiers. The best result was obtained with an SVM classifier with a linear kernel achieving an accuracy of 99.17%, macro sensitivity of 99.17%, and macro specificity of 93.45%. De Freitas Barbosa et al. [32] extended this work by adding a random forest feature selector, but they classified an image as normal (no lesion) or abnormal (cyst, benign lesion, or malignant lesion). They showed that DWNN outperformed InceptionV3 [130], MobileNet [131], ResNet-50 [65], VGG16 [132], VGG19 [132], and Xception [133] in the tasks of lesion detection and classification. The best result was obtained with an SVM classifier with a linear kernel achieving 99% accuracy, 100% sensitivity, and 98% specificity for lesion detection and 97.3% accuracy, 100% sensitivity, and 97% specificity for the lesion classification task.
- Curvelet transform: This [128] decomposes an image into small, elongated wave-like shapes that capture details at different scales and orientations, particularly along curved edges. This technique may be particularly helpful in identifying the vascular structures associated with cancer. Karthiga and Narasimhan [48] applied a curvelet transform [128] to segment 60 thermographic frontal images (30 normal and 30 abnormal) from the DMR-IR dataset before extracting GLCM features. They also extracted first-order statistics, geometrical, and intensity features from the original images. Feature selection was performed using hypothesis testing and several machine learning models were subsequently compared. The best results were achieved with an accuracy of 93.3% and AUC of 94%. They also noted that the GLCM features extracted from the curvet domain increased accuracy by 10% points.
- Histogram of Oriented Gradients (HOG): This [33] is a texture feature extractor that is also applied to the detection of objects in images. An image is split into non-overlapping cells of a predefined size. Regions are defined as a fixed number of cells and may overlap. The gradient is calculated for each pixel and the histogram of all the gradients within each cell is calculated. All the cell histograms of gradients within a region are normalized and concatenated into a single vector and then all the region vectors are concatenated into one vector. See Figure 11.
3.4. Feature Reduction
3.4.1. Feature Selection
3.4.2. Dimension Reduction
3.4.3. Embedded
3.4.4. Bio-Inspired
3.5. Classification
3.6. Performance Assessment
3.7. Key Studies Included in This Review
4. Future Directions
4.1. Benchmark
4.2. Robust Clinical Trials
4.3. Improving Explainability for Radiologists
4.4. Increasing Coverage
4.5. Multi-Modal Methods
4.6. Advances in Artificial Intelligence/Machine Learning
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Acc. | Accuracy |
ACR | American College of Radiology |
AUC | Area Under Curve |
BI-RADS | Breast Imaging Reporting and Data System |
CAD | Computer-aided diagnostic |
CBE | Clinical breast exam |
CSLBP | Center-Symmetric Local Binary Pattern |
CT | Census Transform |
DIT | Dynamic Image Thermography |
DMR-IR | Database for Mastology Research Infrared |
DWNN | Deep-Wavelet Neural Network |
DWT | Discrete Wavelet Transform |
ELM | Extreme Learning Machine |
FD | Fractal Dimension |
FDA | Food and Drug Administration |
FOS | First-Order Statistics |
GA | Genetic Algorithm |
GAN | Generational Adversarial Networks |
GLCM | Gray-Level Co-occurrence Matrix |
GLDM | Gray-Level Dependence Matrix |
GLRLM | Gray-Level Run Length Matrix |
GLSZM | Gray-Level Size Zone Matrix |
HE | Hurst Exponent |
HED | Holistically nested Edge Detector |
HH | Higher High |
HOG | Histogram of Oriented Gradient |
KAN | Kolmogorov–Arnold Network |
KELM | Kernel Extreme Learning Machine |
KNN | K-Nearest Neighbor |
LASSO | Least absolute Shrinkage and Selection Operator |
LBP | Local Binary Pattern |
LDN | Local Directional Number Pattern |
LINPE | Local instance-and-center-symmetric neighbor-based pattern |
LINPE-BL | LINPE Broad Learner |
LR | Logistic Regression |
LSSVM | Least Square Support Vector Machine |
LTP | Local Ternary Pattern |
LTriDP | Local Tridirectional Pattern |
MLP | Multilayer Perceptron |
MRI | Magnetic Resonance Imaging |
NCA | Neighborhood Component Analysis |
NGTDM | Neighborhood Grey Tone Different Matrix |
PCA | Principal Component Analysis |
RBF | Radial Basis Function |
ROI | Region of Interest |
SBBC | Synchronous Bilateral Breast Cancer |
SBE | Self-Breast Exam |
Sens. | Sensitivity |
SFTA | Segmentation Fractal Texture Analysis |
SHAP | Shaplet Additive exPlanations |
SIT | Static Image Thermography |
Spec. | Specificity |
SSM | Structured State-Space Model |
SVM | Support Vector Machine |
UBC | Unilateral Breast Cancer |
ViT | Vision Transformer |
WHO | World Health Organization |
WLD | Web Local Descriptor |
Appendix A
Citation | Description | Equation | Citation | Description | Equation |
---|---|---|---|---|---|
[79] | Energy 1 | [49,61,79] | Mean () | ||
[45,47,48,79] | Min | [49] | Smoothness [190] | ||
[48,79] | Max | [79] | Interquartile Range | ||
[45,61,79] | Entropy | [79] | Mean Absolute Deviation | ||
[45,49,79] | Standard Deviation | [45,48,49,79] | Kurtosis | ||
[79] | Range | [45,48,49,61,79] | Skewness | ||
[79] | Root Mean Square | [49,61,79] | Variance ( |
Citation | Description | Equation |
---|---|---|
[79,80] | Coarseness () | k = {1,2…h: h is a given positive integer} |
[79,80] | Contrast () | |
[79,80] | Directionality 1 () | |
[80] | Line-Likeness () | |
[80] | Regularity 2 | |
[80] | Roughness |
Citation | Description | Equation |
---|---|---|
[5,44,47,48,49,61,68,75,76,79] | Angular Second Momentum 1 | |
[5,44,47,48,49,68,75,76,79] | Contrast | |
[5,44,47,48,49,68,75,76,79] | Correlation | |
[47,49,75,76,79] | Sum of Squares: Variance 2 | |
[49,75,76,79] | Inverse Difference Moment | |
[47,68,75,76,79] | Sum Average () | |
[47,68,75,76,79] | Sum Variance | |
[47,68,75,76,79] | Sum Entropy | |
[47,49,68,75,76,79] | Entropy (H) | |
[47,68,75,76,79] | Difference Variance | |
[47,68,75,76,79] | Difference Entropy | |
[47,68,75,76,79] | Information Measures of Correlation | |
[79] | Maximal Correlation Coefficient |
Citation | Description | Equation |
---|---|---|
[47,68,75,76,79] | Autocorrelation | |
[61] | Contrast | |
[47,61] | Correlation | |
[47,68,75,76,79] | Cluster Prominence | |
[47,68,75,76,79] | Cluster Shade | |
[79] | Cluster Tendency | |
[79] | Difference Average | |
[5,44,47,75,76] | Dissimilarity | |
[5,44,47,48,61,75,76] | Homogeneity I and II | |
[79] | Inverse Difference | |
[79] | Inverse Difference Normalized | |
[79] | Inverse Variance | |
[79] | Joint Average | |
[47,68,75,76,79] | Maximum Probability | |
[47,68,75,76] | Inverse Difference Normalized | |
[47,68,79] | Inverse Difference Moment Normalized |
Citation | Description | Equation | Citation | Description | Equation |
---|---|---|---|---|---|
[68,75,76,79] | Short Run Emphasis (SRE) | [68,75,76,79] | High Gray-Level Run Emphasis | ||
[68,75,76,79] | Long Run Emphasis (LRE) | [68,79] | Short Run Low Gray-Level Emphasis | ||
[68,75,76,79] | Gray-Level Nonuniformity (GLN) | [68,79] | Short Run High Gray-Level Emphasis | ||
[68,75,76,79] | Run Length Nonuniformity (RLN) | [68,79] | Long Run Low Gray-Level Emphasis | ||
[68,75,76,79] | Run Percentage | [68,79] | Long Run High Gray-Level Emphasis | ||
[68,75,76,79] | Low Gray-Level Run Emphasis |
Citation | Description | Equation | Citation | Description | Equation |
---|---|---|---|---|---|
[79] | Gray-Level Nonuniformity Normalized | [79] | Run Variance | ||
[79] | Run Length Nonuniformity Normalized | [79] | Run Entropy | ||
[79] | Gray Level Variance |
Citation | Description | Equation |
---|---|---|
[68,79] | Coarseness | |
[68,79] | Contrast | |
[68,79] | Busyness | |
[68,79] | Complexity | |
[68,79] | Strength |
Citation | Description | Equation | Citation | Description | Equation |
---|---|---|---|---|---|
[79] | Small Dependence Emphasis | [79] | Dependence Entropy | ||
[79] | Large Dependence Emphasis | [79] | Low Gray Level Emphasis | ||
[79] | Gray-Level Nonuniformity | [79] | High Gray Level Emphasis | ||
[79] | Dependence Nonuniformity | [79] | Small Dependence Low Gray Level Emphasis | ||
[79] | Dependence Nonuniformity Normalized | [79] | Small Dependence High Gray Level Emphasis | ||
[79] | Gray-Level Variance | [79] | Large Dependence Low Gray Level Emphasis | ||
[79] | Dependence Variance | [79] | Large Dependence High Gray Level Emphasis |
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Modality | Description | Advantages | Limitations |
---|---|---|---|
Mammography | Uses low-dose X-rays on compressed breasts to identify abnormal growths such as tumors, cysts, or calcifications. | FDA approved as the primary modality. Appropriate for screening and diagnosis. Quick and non-invasive. Well-defined standard [11]. | Less sensitive to women with dense breasts, especially in young women. Radiation exposure. Uncomfortable for women. Cannot use on pregnant women. Costly and less available in developing countries. |
MRI | Uses a magnetic field and radio waves to create a detailed image of the breast after injecting the patient with IV contrast dye. | Detecting suspicious masses. Well-defined standard [11]. Greater sensitivity than mammography. | FDA approved as an adjunct modality. Expensive and less available in developing countries. |
Ultrasound | Uses high-frequency sound waves to detect changes in the breasts. Used as an adjunct to mammography to detect and help classify abnormalities and guide biopsy. | East of use. Real-time imaging. Differentiates cysts from solid masses. Well-defined standard [11]. Sensitive to women with dense breasts. Can diagnose benign palpable masses. Can be used with breast implants. Radiation free. Painless and no discomfort. Can use on pregnant and lactating women. | FDA approved as an adjunct modality. Poor visibility to deep lesions. |
Thermography | Uses thermal radiation emitted from breasts to detect differences. | Mobile and ease of use. Real-time imaging. Sensitive to women with dense breasts. Can be used with breast implants. Radiation free. Contactless and painless. Cost-effective for developing countries. | FDA approved as an adjunct modality. Multiple standards [12]. Patient temperature differences due to hormones, exercising, pregnancy, and menopausal cycle impact results. Requires temperature and humidity-controlled environment. Limited trials. |
Symbol | Description |
---|---|
I | Symbol representing an image. |
L | Number of intensity levels in image I. |
Intensity level of pixel in image I at horizontal location and vertical location . | |
Number of rows and columns in image I. | |
The intensity levels for an L-level digital image, where . | |
Number of pixels in image f with intensity level . | |
is the histogram of intensity values in f. | |
is the normalized histogram of intensity values in f. |
Modified Ville Marie [36] | Thermobiological Grading System [37] | Twenty Point Thermobiological [38] | |||
---|---|---|---|---|---|
Grade | Description | Grade | Description | Grade | Description |
IR1 | Absence of any vascular pattern to mild vascular symmetry | TH1 | Symmetrical, bilateral, and nonvascular (non-suspicious, normal study) | TH1 | Normal Symmetrical Non-Vascular |
IR2 | Significant buy symmetrical vascular pattern to moderate vascular asymmetry, particularly if stable | TH2 | Symmetrical, bilateral, and vascular (non-suspicious, normal study) | TH2 | Normal Symmetrical Vascular |
IR3 | One abnormal sign | TH3 | Equivocal (low index of suspicious) | TH3 | Questionable |
IR4 | Two abnormal signs | TH4 | Abnormal (moderate index of suspicion) | TH4 | Abnormal |
IR5 | Three abnormal signs | TH5 | Highly abnormal (high index of suspicion) | TH5 | Very Abnormal |
Citations | Designer | Public | Classes | Protocol | Camera |
---|---|---|---|---|---|
[5,29,41,42,43,44,45,46,47,48,49,50,51,52,53] | DMR-IR [39] | Yes | Normal: 184, Sick: 105, Unknown:4 | DIT, SIT | FLIR SC620 |
[41,42,43,54] | Ann-Arbor [55] | Yes | Normal: 4, Sick: 11, 15 images | SIT | Not specified |
[31,32,56,57] | HC-UFPE [31,57] | No | Benign Lesion: 121, Malignant Lesion: 76, Cyst: 72, No Lesion: 66; 1052 images | SIT | FLIR S45 |
[53] | Mendeley [21] | Yes | Normal: 0, Benign: 84, Malignant: 35 | SIT | FLIR A300 |
None known | Unnamed [58] | Yes | Normal: 6, High Risk: 2, Malignant: 11 | SIT | Not specified |
[51] | DBT-TU-JU [59,60] | No | Normal 45, Benign: 36, Malignant: 13, Unknown: 6 | SIT | FLIR T650sc |
[61] | Unnamed [61] | No | Normal: 30, Abnormal: 20 | SIT | FLIR 74 |
Citation | Method | Features | Advantages | Limitations |
---|---|---|---|---|
[45,48,49,61,79] | First-Order Statistics [74,79] | Estimate properties of individual pixels (mean, energy, entropy, kurtosis, etc.) | Statistical summary of intensity information. Computational efficient. Works well for homogenous images. | No spatial and local information. Ineffective for multi-texture images. Cannot identify lesion location. |
[79] | Tamura [80] | Globally quantify coarseness, contract, directionality, likeness, roughness, and regularity. | Mimic human perception. Classifies texture. Scale invariant. Works well for homogenous images. | May not distinguish fine texture details. Ineffective for multi-texture images. Cannot identify lesion location. |
[5,44,45,48,49,56,57,61,68,79] | Co-occurrence Matrix [78] | Capture frequencies of co-located values and calculate 2nd-order statistics (energy, entropy, contrast, homogeneity, etc.). Includes GLCM [78], GLRLM [81,82], NGTDM [83], GLDM [84], GLSZM [85], and GLDM [79,84]. | Describes spatial relationships between pixels. Identifies surface pattern. Invariance to gray-level transformation. | Does not detect textures based on large primitives. Sensitive to scale and rotation. Restricted to a single direction. Dependent on manual choice of parameters |
[44,45,46,51] | Non- parametric local transform [86] | Encodes local pixel intensity relationships. Includes LBP [87], CT [86], LTP [5,88], and LDN [89] texture features. | No probability distribution requirement. Robust to varying illumination. Captures local texture. Computational simplicity. | Noise sensitive. Limited global context. High dimensional vectors. |
Method | Cite | Type | Description |
---|---|---|---|
Feature Selection | [49,68] | t-test [137] | Select features with significant differences in class means. |
[32] | Random Forest [138] | Select features that maximally reduce impurity. | |
[44] | Neighborhood Component Analysis [139] | Select features by maximizing an objective function. | |
[57] | Forward Selection [97] | Add features until the target objective does not improve. | |
[57] | Correlation Method [140] | Retain uncorrelated features. | |
[57] | Objective Dialectical Method [141] | Select features that optimally balance relevance and redundancy. | |
Dimension Reduction | [45,57,68] | Principal Component Analysis (PCA) [99] | Reduce dimensions by mapping signals to orthogonal components and select those with the highest variance. |
[45] | Independent Component Analysis [142] | Map features to fewer statistically independent components. | |
[45] | Locality Preserving Projections [143] | Preserves local structure in lower dimensional space. | |
Embedded | [79] | Adaptive LASSO Regression [144] | Select features by applying L1 regression penalizing absolute values of coefficients. |
Bio- inspired | [57] | Genetic Algorithm [93] | Select feature subset evolved from feature population that maximizes fitness function. |
[46,57] | Particle Swarm Optimization [145] | Select features by simulating the collective movement of particles. | |
[46] | Spider Monkey Optimization [146] | Select features by simulating the foraging behavior of monkeys. | |
[57] | Ant Colony Search [147] | Select features by simulating the foraging behavior of ants. | |
[57] | Bee Colony Search [148] | Select features by simulating the foraging behavior of bees. | |
[50] | Binary Grey Wolf Optimizer [119] | Select features by simulating the behavior of grey wolves. | |
[50] | Firefly Algorithm [149] | Select features by simulating the behavior of fireflies. |
Classifier Method | Description |
---|---|
Support Vector Machine (SVM) [30] | Find a hyperplane that maximizes class separation. |
Logistic Regression (LR) [96] | Maximum likelihood estimator of sigmoid function. |
Decision Tree [96] | Recursive partition feature space to identify class. |
Random Forest [97] | Train multiple trees on different feature and data subsets. |
Multilayer Perceptron (MLP) [96] | A multilayered feedforward neural network with a non-linear activation function. |
Naïve Bayes [96] | Identify class by maximum posterior probability. |
AdaBoost [152] | Reduce misclassified instances by cascading multiple weak classifiers. |
Least Square Support Vector Machine (LSSVM) [153] | Least square SVM that solves a set of linear equations instead of classical SVM technique. |
Extreme Learning Machine (ELM) [98] | Single-layer feedforward neural network updating weights using Moore–Penrose pseudo-inverse. |
Extreme Gradient Boosting (EGB) [129] | Reduce residual error by cascading weak decision trees. |
Description | Equation | Advantages | Limitations |
---|---|---|---|
Accuracy | Simple to understand and compute. Useful as a baseline metric. | Misleading for imbalanced datasets. Does not distinguish the type of error. | |
Sensitivity (Recall) | Works for imbalanced datasets. Good metric for high-risk use cases. Compliments Precision and Specificity. | Ignores false positives, therefore may lead to high noise. Not complete alone. | |
Specificity | Works for imbalanced datasets. Good metric for avoiding false alarms. Compliments Sensitivity. | Ignores false negatives, therefore may lead to high undetected positives. Not complete alone. | |
Precision | Works for imbalanced datasets. Useful when positive is costly. Complements recall. | Ignores false negatives, therefore may lead to high undetected positives. May lead to under detection. Not complete alone. | |
F-Score | Works better for imbalanced datasets than accuracy. One metric that balances precision and recall. Widely adopted and understood. | Ignores true negatives. Hides precision and recall metrics. |
Author(s) | Dataset | Leaked | Feature Extraction | Feature Reduction | Classifier | Performance |
---|---|---|---|---|---|---|
Madhavi and Thomas [68] | DMR-IR (63 patients) | No | GLCM, GLRLM, GLSZM, and NGTDM | t-test into Kernel PCA | LSSVM | Acc: 96% Sens: 100% Spec: 92% |
Rodrigues da Silva et al. [56] | HC-UFPE (336 patients) | ? | GLCM and Zernike moments | None | ELM | Acc: 94.00% ± 2.8 Kappa: 93.23% ± 3.1 |
Resmini et al. [5] | DMR-IR (80 patients) | GLCM | GA | SVM | Acc: 94.61% Sens: 94.61% Spec: 94.87% | |
Pereira et al. [57] | HC-UFPE (336 patients) | ? | GLCM and Zernike moments | None | SVM | Acc: 91.42% ± 2.93 Macro Sens: 91.12% Macro Spec: 91.36% |
Josephine et al. [61] | Private (50 images) | ? | FOS and GLCM | None | AdaBoost | Acc: 91% F1-Score: 89% |
Pramanik et al. [51] | DMR-IR (226 patients) | ? | LINPE [51] | Training-based | LINPE-BL [51] | Acc: 96.9% Sens: 95.7% Spec: 97.2% |
Chebbah et al. [49] | DMR-IR (90 images) | ? | FOS, GLCM, and blood vessels | t-test | SVM | Acc: 92.2% Sens: 86.7% Spec: 98.3% |
Mishra and Rath [79] | DMR-IR (56 patients) | ? | FOS, GLCM, GLRCM, NGTDM, GLSZM, GLDM, and Tamura | Adaptive LASSO | SVM | Acc: 96.79% Precision: 98.77% Recall: 93.02% F1-Score: 95.81% |
Author(s) | Dataset | Leaked | Feature Extraction | Feature Reduction | Classifier | Performance |
---|---|---|---|---|---|---|
Hakim and Awale [52] | DMR-IR (255 images) | ? | HE, FD, and lacunarity | None | Naïve Bayes | Acc: 94.53% Sens: 86.25% Spec: 97.75% |
Dey et al. [41] | DMR-IR (85 patients) Ann Arbor (16 patients) | No | HE and FD | None | Ensemble | Acc: 96.08% ± 3.87 Sens: 100% ± 0 Spec: 93.57% ± 7.29 |
Moradi and Rezai [50] | DMR-IR (200 images) | ? | SFTA [117] | Firefly Algorithm to Binary Grey Wolf Optimizer | Decision Tree | Acc: 97% Sens: 98% Spec: 96% |
Author(s) | Dataset | Leaked | Feature Extraction | Feature Reduction | Classifier | Performance |
---|---|---|---|---|---|---|
Abdel-Nasser et al. [47] | DMR-IR (56 patients) | No | HOG | None | MLP | Acc: 95.8% Precision: 94.6% Recall: 97.1% F1-Score: 95.4% |
Gonzalez-Leal et al. [45] | DMR-IR and others (1793 patients) | No | FOS, GLCM, LBP, and HOG | Kernel PCA | LR | AUC: 78.5% |
Al-Rababah et al. [127] | DMR-IR (47 patients) | ? | DWT into HOG | None | SVM | Acc: 98.0% Sens: 97.7% Spec: 98.7% |
Karthiga and Narasimhan [48] | DMR-IR (60 patients) | ? | FOS, GLCM, and curvelet transform to GLCM | Hypothesis testing | SVM | Acc: 93.3% AUC: 94% |
de Santana et al. [31] | HC-UFPE (336 images) | ? | DWNN | None | SVM | Acc: 99.17% Macro Sens: 99.17% Macro Spec: 93.45% |
De Freitas Barbosa et al. [32] | HC-UFPE (336 images) | ? | DWNN | Random Forest | SVM | Acc: 99% Sens: 100% Spec: 98% |
Gama et al. [122] | DMR-IR (80 patients) | ? | Canny edge and HED | None | EGB | Acc: 97.4% Precision: 95% Recall: 100% F1-Score: 97% |
Garia and Muthusamy [44] | DMR-IR (1000 images) | No | HOG | NCA | Random Forest | Acc: 98.00% Precision: 97.05% Recall: 99:00% F1-Score: 98.01% |
Dihmani et al. [46] | DMR-IR (56 patients) | No | HOG, LBP, Gabor filter, and Canny Edge | Hybrid Spider Monkey Optimization | SVM | Acc: 98.27% F1-Score: 98.15% |
Youssef et al. [29] | DMR-IR (90 patients) | ? | Image enhanced with Gabor filter, Canny edge, and HED to HOG fused with Resnet-50 + MobileNet | PCA | EGB | Acc: 96.22% Sens: 97.19% Spec: 95.23% |
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Ryan, L.; Agaian, S. Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches. Bioengineering 2025, 12, 639. https://doi.org/10.3390/bioengineering12060639
Ryan L, Agaian S. Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches. Bioengineering. 2025; 12(6):639. https://doi.org/10.3390/bioengineering12060639
Chicago/Turabian StyleRyan, Larry, and Sos Agaian. 2025. "Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches" Bioengineering 12, no. 6: 639. https://doi.org/10.3390/bioengineering12060639
APA StyleRyan, L., & Agaian, S. (2025). Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches. Bioengineering, 12(6), 639. https://doi.org/10.3390/bioengineering12060639