## 2 Literature Review
[36] Extracted handcrafted features such as textures, color, and shape from BC Histopathological images; classified these features with the deep neural network using softmax. Furthermore, the author applied data augmentation to address the problem of overfitting. Experimental results demonstrated that the projected approach accomplished an accuracy of 97.87% for 40x. [PERSON] and [PERSON] [37] compared handcrafted features with the proposed XceptionNet model for features extraction for BC classification. The XceptionNet used as feature extractor and SVM as a classifier outperformed the handcrafted feature and achieved a classification accuracy of 96.25% for a 40x level of magnification. [PERSON] et al. [38] proposed a unified automatic framework for breast tumor segmentation and detection based on segmentation leading to classification. They utilized SLIC and K Means++ for tumor segmentation and VGG16, VGG19, DenseNet121, and ResNet50 for breast tumor classification. Furthermore, SLIC, UNET, and VGG16 outclassed all other combined combinations. [PERSON] et al. [39] proposed a new hybrid approach for BC detection. On images from the dataset, the researchers used an adaptive median filter to reduce noise and some preprocessing approaches to remove the background. They also modify the segmentation-related parameter values for K Means and the Gaussian mixture framework. The accuracy of the suggested hybrid model was 95.5%. [PERSON] et al. [40] presented the TL approach, and six performance elevation matrices are used in the DL model suggested for automatically detecting BC. They deployed five pre-train DL models for feature extraction and classification. These models were the Inception-V3 ResNet, the VGG19 ResNet, the VGG16 ResNet, and the Inception-V3. It has been shown that the VGG16 model is suitable for BC detection, with an accuracy of 98.96 percent. A CNN-based hybrid model for BC classification was proposed by [PERSON] et al. in [41]. The Mobilenetv2, Resnet50, Alexnet, and Resnet50 neural networks extracted information from BC ultrasound images. The cooperation of the mRMR technique is used to link the extracted characteristics together in order to increase accuracy. The support vector machine (SVM) is then implemented to categorize these features. The suggested hybrid model attained an accuracy of classification of 95.6%. Convolutional Neural Network (CNN) and Transfer Learning are used in [PERSON] et al. [42] suggested method to improve diagnosis by boosting the efficacy and accuracy of early breast cancer detection. Instead of creating the entire model from scratch, the thought process entailed leveraging a pre-trained model that already had certain weights given. This research primarily focuses on a Transfer Learning Model built on ResNet101 and used with the ImageNet dataset. The proposed framework gave us a 99.58% accuracy rate. [PERSON] et al. [43] In their research, they intended to detect BC from ultrasound. In their research, they used two different data sets. They combined multiple CNN algorithms using the image fusion approach. In their initial attempt, they claimed to have attained an accuracy of 91.10%. They obtained 94.62% in their first dataset and 94.62% in their second [PERSON] et al. [44] Examined InceptionV3, ResNet50, and XceptionNet and presented a basic model for identifying breast malignancies from the breast Neural Computing and Applications 123 ultrasound images dataset, consisting of three convolutional layers. There were 2058 images in the dataset, including benign 1370, while malignant 688 samples. According to their research, InceptionV3 had the highest accuracy (85.13%). [PERSON] el al. [45] the offered approach is composed of a few sequential steps. The breast ultrasound data are first enhanced, and then a DarkNet-53 deep learning model is used to retrain the data. The features were then retrieved from the pooling layer, and the two alternative optimization techniques, such as the reformed BGWO and the reformed DE, were used to choose the best feature.