# Optimal Histopathological Classification Factors for Deep Learning-Based Breast Cancer Prediction
## 2 Literature Review  
Numerous methods have been proposed in the literature that aim to predict breast cancer using ML, histopathological images, and various data preprocessing approaches. Some researchers believe data preprocessing techniques are essential for accurately predicting disease states from medical images. Others believe ML algorithms can be used effectively without any preprocessing steps. Still, others think combining data preprocessing and ML may produce the most accurate predictions possible. All of the papers chosen for this study's literature review use histopathological images and ML algorithms to identify cancerous tissue, mainly in the breast. The work by [PERSON] et al. (2016) detects Invasive Ductal Carcinoma (IDC) (a type of breast cancer) using Residual Convolution Networks (RCNs) to classify the IDC-affected histopathological images from the normal images. First, the microscopic RGB images are converted into a seven-channel image matrix, then fed to RCNs. The work claims that the proposed model produces an accuracy of over 90%. The study by [PERSON] et al. (2018) emphasizes that feature extraction from images plays a prominent role in image processing. Their study applies a combination of histograms of oriented gradient and Canny Edge Detection (CED) techniques for extracting features. Then they use PCA to reduce the dimensionality of the extracted features. PCA output is input for SVM and LR. The experiment shows 94% correct detection of malignant patients. The authors in [PERSON] et al. (2018) investigated whether combining histopathological images with commonly available patient data (such as age, sex, and anatomical site of the lesion) could increase the performance compared with CNNs alone, referred to as standard CNNs. Their results showed that standard CNNs achieve better accuracy in most cases than patient data integration with the image. The authors in [PERSON] et al. (2017) introduce an approach to quantify and classify breast tissue samples based on features extracted from the intensity histogram, co-occurrence matrix, Shannon, Renyi, Tsallis, and Kapoor entropies. The obtained feature vector is used as input to the RF and Sequential Minimal Optimization (SMO) algorithms. The study by [PERSON] et al. (2017) claims to achieve significant results in the Area Under the Curve (AUC) performance measurements. The authors in [PERSON] et al. (2018) employ two ML algorithms for comparative analysis: SVM and LR. The model is trained separately for various image magnification factors, i.e., 40x, 100x, 200x and 400x. The findings of [PERSON] et al. (2018) demonstrate that the ResNet50 has achieved maximum accuracy for LR compared to SVM in magnification factor. In addition, results show that the performance of CNNs+LR is slightly better than CNNs+SVM for classifying benign and malignant classes. The work by [PERSON] et al. (2018) utilizes CNNs to extract the features and create a model. The Fuzzy Classifier is utilized in [PERSON] et al. (2018) to quantify and classify cancerous cells based on color. The color analysis is based on various colors, such as Hue Saturation Value (HSV), rather than specific color values. Their findings can also be used in the morphological processing of the classified binary image to locate, count, and confirm particular kinds of regions and tissues in the biopsy sample. The study by [PERSON] et al. (2018) exploits DenseNet-201 as a feature extraction method. Their study achieves above 90% accuracy while the precision and recall, ML performance metrics, are 0.90 and 0.99, respectively; the F1-score is reported to be 89%. The authors in [PERSON] et al. (2018) apply two procedures: first, they use ML algorithms for image classification; second, they use segmentation algorithms for detecting tumorous cells; and afterward, they use SVM and CNNs algorithms for tumorous classification. The classifiers are examined based on sensitivity, specificity, accuracy, precision, and F1-score parameters.