# Optimal Histopathological Classification Factors for Deep Learning-Based Breast Cancer Prediction
## 1 Introduction  
The most common histopathological dye is hematoxylin and eosin (H&E). This stain colors cell nuclei blue and cytoplasm pink. Cancer cells typically appear abnormal when stained with H&E; the cell structure's appearance helps the pathologist distinguish them from normal cells. A biopsy or surgical excision is the most common source of a tissue specimen. A needle is inserted into the tumor during a biopsy, and cells are extracted for examination. Surgical excision is when the entire tumor or lesion is surgically removed and sent to a pathologist for analysis. Pathology microscopes are equipped with different magnification capabilities: low-power magnification ranges from 2x to 10x; medium-power magnification ranges from 10x to 40x; and high-power magnification ranges from 40x to 400x, depending on the pathologist's needs. By examining different magnification factors, the histopathological can discover unique features of the deforming cells, which helps diagnose whether the cells are malignant or benign. Microscopic tissue analysis assists in determining the disease stage and guiding treatment decisions. While histopathology is critical, it can also be prone to diagnostic error because histopathological often look at slides for a long time and become tired. However, errors can still occur with the utmost care and precision. The most recent study on histopathological error statistics found that while pathologists make an estimated 1 in 10,000 mistakes when diagnosing actual cancer cells, errors rise to 1 in 3 when diagnosing earlier stages of cancer cells due to their indistinct cancer cell features ([PERSON] et al. [1]). The National Academy of Medicine (NAM) [2] found that pathology mistakes account for up to 10 percent of hospital adverse patient events. Many factors can contribute to pathology errors, including inadequate training, poor communication among healthcare providers, and faulty equipment. Manyexperts believe that the number of actual errors committed is likely far higher than reported due to underreporting by physicians and patients. With continued research into new techniques and technologies, it is believed that Artificial Intelligence (AI) and Deep Learning (DL) are promising technologies to help pathologists reduce diagnostic errors. Convolution Neural Networks (CNNs) are a type of Machine Learning (ML) that uses neural networks to comprehend how to identify patterns in images. CNNs have made it possible to detect cell cancer biomarkers more accurately than ever. For example, one study showed that CNN-based algorithms could correctly pinpoint breast cancer metastases with a precision of 90% and above most of the time--significantly outperforming human pathologists. Many ideas in the literature are based on using ML and histopathological images, combined with various data preprocessing techniques, to predict breast cancer. Authors in [PERSON] et al. [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [6], and [PERSON] et al. [7] have applied Support Vector Machine (SVM), while the works by [PERSON] et al. [7] and [PERSON] et al. [8] utilize Logistic Regression (LR). The studies by [PERSON] et al. [6], [PERSON] et al. [9], [PERSON] et al. [10], [PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14] predict breast cancer using CNNs. The study by [PERSON] et al. [15] utilizes CNNs to predict a malignant tumor of the digestive system. Residual Neural Networks (RNNs) are implemented in the work of [PERSON] et al. [16], the Random Forest (RF) algorithm is applied in [PERSON] et al. [17] and [PERSON] et al. [6]. Similarly, fuzzy classification is used in [PERSON] et al. [18]; K-means in [PERSON] et al. [5]; SENet DL in [PERSON] et al. [19]; the Naive Bayes (NB) and Decision Tree (DT) in [PERSON] et al. [8].