Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms
Abstract
:Simple Summary
Abstract
1. Introduction
2. Technologies Used to Obtain Breast Tissue Images
2.1. Mammography
2.2. Ultrasound
2.3. Magnetic Resonance Imagining (MRI)
2.4. Other Approaches
3. Image Processing and Classification Strategies
3.1. ROI Estimation
3.2. Feature Extraction
3.3. Classifiers
3.3.1. Unsupervised Classifiers
3.3.2. Supervised Classifiers
3.4. Artificial Intelligence-Based Classifiers
4. Recent Image Generation Techniques
Infrared Thermography (IRT) Applied to Breast Cancer
- Individual factors: everything that has to do with the patient’s conditions, such as age, sex, height, medical history, among others. As well as the inclusion and exclusion criteria. An aspect of vital importance is the emissivity of humans, which is 0.98 [164].
- Technical factors: it has to do with everything related to the technology used during the study, such as the thermal imager (considering the distance from the lens to the patient), the protocol, the processing of the medical thermal images obtained, as well such as feature extraction and subsequent analysis.
- Environmental factors: room position (it should be located in the area of the lowest possible incidence of light), temperature, relative humidity of the space where the thermographic images are to be taken, as well as the patient’s air conditioning time.
5. Recent Classification Algorithms
5.1. Autoencoders
5.2. Deep Belief Networks (DBF)
5.3. Ladder Networks
5.4. Deep Neural Network (DNN)-Based Algorithms
6. Concluding Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Imagining Technique | Advantages | Disadvantages | Recommended Population | Some Types of Cancer Detected | Sensitivity and/or Specificity |
---|---|---|---|---|---|
Mammography | 1. Equipment is widely available worldwide. 2. Methods, such as tomosynthesis, can improve the specificity and sensibility of the technique with patients that have dense breasts [10] | 1. The rate of both false positive and false negatives increases since there is no possibility to determine if the masses are benign 2. The procedure used to obtain the images could be bothersome. 3. Dense breasts or young patients are not indicated to use this imaging technique. | Women whose age is greater than 40 years, have low-dense breast and an average risk of contracting the disease. | 1. Ductal Carcinoma in Situ 2. Invasive Breast Cancer. | Sensitivity up to 85%. |
Ultrasound | 1. Can be used in young patients or have dense breast. 2. The equipment used is available in most of the hospitals | 1. Calcifications could not be detected. 2. Sensitivity depends on the operator ability to interpret the images 3. False-positivity rate is an issue. | Women with heterogeneously or extremely dense breast tissue [38,39]. Women that are pregnant or lactating [40]. | 1. Ductal Carcinoma in Situ. 2. Invasive ductal carcinoma | Sensitivity ranging between 40–75% in younger high-risk women [40]. |
Magnetic Resonance Imaging | 1. Effective for detecting suspicious masses in high-risk population [10]. 2. The breast tissue density is no longer an issue [38,39,40]. 3. Multifocal lesions can be detected [10,41] | 1. Equipment is only available in specialized hospitals. 2. Expensive 3. False positive findings are an important concern [41] | 1. Women that may carry mutation in ATM, BRCA1, BRCA2, CHEK2, PALB2, PTEN, TP53 genes. 2. Women that had radiation therapy in the chest zone during the childhood. | 1. Ductal in situ carcinomas 2. Invasive ductal carcinomas. 3. Invasive lobular carcinomas 4. Invasive mammary carcinomas with mixed ductal and lobular features [24] | Sensitivity ranging from 83 to 100% [42,43,44]. |
Type of Classifier | Classifier | Advantages | Disadvantages | Number of Images | Performance Metrics |
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Unsupervised | K-means |
|
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Hierarchical Clustering |
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| |
Supervised | Decision Trees |
|
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| |
Random Forest |
|
|
|
| |
AdaBoost |
|
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Support Vector Machines |
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Artificial Neural Networks |
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Convolutional Neural Networks |
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|
Authors | Number of Patients | IR System | Image Processing and Classification Algorithms | Accuracy (%) | Room Temperature (°C) | Acclimation Time (min) | |
---|---|---|---|---|---|---|---|
Features | Classification | ||||||
Ekici and Jawzal [165] | 140 | FLIR SC-620 | Bio-data, image analysis, and image statistics | CNNs optimized by Bayes algorithm | 98.95 | 17–24 | 15 |
AlFayez et al. [166] | Public dataset DMR-IR | Geometrical and textural features | Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP) | ELM—100 MLP—82.2 | Public dataset DMR-IR | ||
Rani et al. [167] | 60 | FLIR T650SC | Temperature and intensity | SVM with Radial basis function kernel | 83.22 | 20–24 | 15 |
Saxena et al. [168] | 32 | FLIR A320 | ROI thermal | Cut-off value | 88 | 22 ± 0.5 | Not specified |
Tello-Mijares [169] | 63 | FLIR SC-620 | Shape, colour, texture, and left and right breast relation | CNN | 100 | 20–22 | 15 |
Garduño-Ramón et al. [170] | 454 | FLIR A300 | Temperature | Difference of temperature | 79.60 | 18–22 | 15 |
Raghavendra et al. [171] | 50 | Thermo TVS200 | Student’s t-test based feature selection algorithm | Decision Tree | 98 | 20–22 | 15 |
Lashkari et al. [172] | 67 | Thermoteknix VisIR 640 | 23 features, including statistical, morphological, frequency domain, histogram and Gray Level Co-occurrence Matrix | Adaboost, SVM, kNN, Naive, PNN | 85.33 and 87.42 | 18–23 | ice test: 20 min |
Francis et al. [173] | 22 | med2000™ IRIS | Statistical and texture features are extracted from thermograms in the curvelet domain | SVM | 90.91 | 25 | 15 |
Milosevic et al. [174] | 40 images | VARIOSCAN 3021 ST | Texture measures derived from the Gray Level Co-occurrence Matrix | K-Nearest Neighbor | 92.5 | 20–23 | Few minutes |
Araujo et al. [175] | 50 | FLIR S45 | Thermal interval for each breast | Linear discriminant classifier, minimum distance classifier, and Parzen window | - | 24–28 | At least 10 min |
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Basurto-Hurtado, J.A.; Cruz-Albarran, I.A.; Toledano-Ayala, M.; Ibarra-Manzano, M.A.; Morales-Hernandez, L.A.; Perez-Ramirez, C.A. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers 2022, 14, 3442. https://doi.org/10.3390/cancers14143442
Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers. 2022; 14(14):3442. https://doi.org/10.3390/cancers14143442
Chicago/Turabian StyleBasurto-Hurtado, Jesus A., Irving A. Cruz-Albarran, Manuel Toledano-Ayala, Mario Alberto Ibarra-Manzano, Luis A. Morales-Hernandez, and Carlos A. Perez-Ramirez. 2022. "Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms" Cancers 14, no. 14: 3442. https://doi.org/10.3390/cancers14143442