## 4 Related Work
They converted 680 thermal images to grayscale extracted from a Visual lab-IR dataset and then pre-processed, segmented, and classified them. They achievedpredictive accuracy of 95.8% with 680 thermograms. This model outperformed the study in [21], which achieved an accuracy of 93.30% with 50 thermograms. In addition, in comparison to a study in 2012 [22] that used the DT and fuzzy classifiers, it achieved the highest average accuracy of 93.30% and a significant improvement was achieved in this study. In [23] the authors investigated the combination of GVF breast segmentation and CNN classification to detect breast cancer. They used 63 breast images obtained from the DMR-IR dataset as normal and abnormal. The model was evaluated using a 2-fold cross-validation approach; it obtained 100% accuracy, sensitivity, and specificity, and achieved a better performance than tree random forest (TRF), MLP, and Bayes Network. The authors of [24] applied a multi-input classifier model based on CNNs for early detection of breast cancer that combines thermal images of different opinions with the personal and clinical data of 287 patients. They compared the performance of seven models; the results demonstrated that the M.4 ncd model had the best accuracy of 97%, AUC ROC of 0.99, a specificity of 100%, and a sensitivity of 83%. Furthermore, the M.4 ncd model results were compared with the results of other studies in the literature, and the model outperformed them in AUC ROC and specificity measures. In [25], CNNs were trained to classify thermal images through static and dynamic protocols. The DMR-IR dataset that was classified through the static protocol contained 300 thermal images. Using the dynamic protocol, 2740 images were classified. The proposed method obtained competitive results in both protocols. The static protocol achieved 98% accuracy for color images and 95% for grayscale, while the dynamic protocol achieved 95% for color images and 92% for grayscale. It outperformed other methods applied to the same dataset. The reviewed studies using thermal breast cancer images demonstrated that, compared to other image modalities, thermal images achieved good results when used for the detection of breast cancer. Additionally, they were used with both ML and DL methods. As we have seen, the previous reviewed studies gave equal importance or \"attention\" to all patches of an image and did not give special attention to prominent areas. These studies were conducted using AMs integrated with DL algorithms. In [26], the authors introduced a new model of DL for the diagnosis and classification of histopathology images. This consisted of CNN and RNN models and used a parallel structure of two different types of neural networks--a CNN (DenseNet) and an RNN (LSTM)--with AMs conducted on three datasets. They applied the models on three datasets, namely, BACH2018, Bioimaging 2015, and Extended Bioimaging 2015. The accuracy rates achieved on those three datasets were 92%, 96%, and 97.5%, respectively. Similarly, in [27], the BreastNet model was built based on CNNs and AMs to classify 7909 tissue images extracted from the BreakHis dataset. The best results were achieved using 200X images with an accuracy of 98.51%, sensitivity of 98.70%, and F1-score of 98.28%. In another study [4], the authors introduced a new CNN-based approach that includes two separate attention units to learn a more effective feature for classifying 7909 histopathology images from the BreakHis dataset. The model achieved 98% accuracy when amplifying 100\(\times\), which outperformed the latest methods performed on the same dataset by a large margin. The authors of [28] suggested a CNN-based, fine-grained image model to extract features and a novel AM for localizing objects performed on histology images from Peking Union Hospital and the BACH benchmark dataset from the 2018 Grand Challenge in Breast Cancer Histology images.