## 4 Related Work  
This section presents a review of the current literature on breast cancer diagnosis and classification using thermal imaging. In the past few years, a significant number of studies have been conducted using ML and DL to diagnose breast cancer in mammography and histopathology imaging. A hybrid method using a CNN and LSTM was proposed in [12] to classify 7909 histological images extracted from the BreakHis dataset, after features are extracted using a proposed model fed into softmax and SVM for the decision stage. The best precision of 96% was achieved on a 40\(\times\) dataset, and the best accuracy of 91% on 200\(\times\), which was comparable with the most recent findings. In [13], the authors proposed a new CNN architecture inspired by the U-net structure of the two CBIS-DDSM mass and macrocalcification mammography datasets, in which whole data from both datasets of 692 mass images and 603 macrocalcification images were trained, while 202 and 152 were tested, respectively. The model achieved an accuracy of 94.31%. A new model called IRRCNN was proposed in [14] based on a CNN to classify breast cancer (benign/malignant) based on 7909 histopathological images extracted from the BreakHis dataset and to verify the performance of the model. For the test, the model achieved 91.4%, compared to the methods applied to the open-source dataset. The BC-DROID model enabled one-step automatic detection and classification using a CNN; in [15] the model was trained on 10,480 complete mammograms extracted from DDSM data, achieving a detection accuracy of 90%, a classification accuracy of 93.5%, and an AUC of 92.315%. In [16], a gray-level co-occurrence matrix and a gray-level run-length matrix extracted and fed two inputs into a hybrid deep learning model of a CNN and RNN called a CRNN for the detection of mammographic breast cancer; the result and AND operation outputs of the classifier achieved a diagnostic accuracy of 90.59%, which outperformed the traditional models. The rapid growth in the development of infrared cameras has recently led to an expansion of research to diagnose and classify breast cancer using thermal images. Many studies have been conducted using ML and DL algorithms on thermal images. The authors of [17] classified 1052 thermogram images from the University Hospital at The Federal University of Pernambuco. They used several models such as the Bayes network, naive Bayes, SVM, J48 decision tree, multi-layer perceptron (MLP), random forest (RF), random tree (RT), and ELM. The results showed that MLP gave promising results compared to the other classifiers, with an accuracy of 73.38%, a kappa index of 0.6007, a sensitivity of 78%, and a specificity of 88%. The test was also performed using a 10-fold cross-validation method with a kappa index of 0.6402, and the accuracy was increased to 76.01%. The overall efficiency of the system was 83%. In another study [18], the features SSigFS, FStat, and STex were used to improve the performance of ANN, SVM, KNN, and DT classifiers in diagnosing thermal images from the DBT-TU-JU and DMR-IR datasets. The classifiers' performances in both databases were compared; in DBT-TU-JU, SVM-RBF and ANN achieved the highest accuracy of 84.29%, while in the DMR-IR dataset, ANN and SVM-linear achieved the highest accuracy of 87.50%. On the other hand, DL algorithms, especially a CNN, have been applied on thermal images and have shown a competitive performance in diagnosing breasts. In one of the studies applying DL on thermal images [19], a CCN was applied for the first time to classify thermal images and was optimized by the Bayes optimization algorithm. It was trained on 1116 images extracted from the DMI dataset and achieved an accuracy of 98.05%. Compared with previous studies using the same dataset, with different features and classifiers, the model achieved a competitive performance on more images. In another study [20], the authors diagnosed breast cancer from thermal images using a DCNN model.