# CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis
###### Abstract
Figure 1: Distributing the Cases and Deaths of the 10 Prevalent Cancers in 2020 for Both Sexes [3]. though mammography is used extensively, interpreting its images has challenged specialists. For instance, false positives may cause patient anxiety [8] and unimportant follow-ups, as well as invasive diagnostic proceedings. The types of cancer that are not identified at screening may be unidentifiable until the advanced stages, when they are hard to treat [9]. In the 1990s, mammography had computer-aided detection (CAD). Since then, many assisting tools have been adopted for medical purposes [10]. Although they have been thought of as promising [11; 12], this generation of software did not succeed in obtaining a better performance compared to readers in actual settings [6; 12; 13]. Lately, several developments have resulted in the reissuance of the field, because of the successful attempts of deep learning. Scholars and researchers employed several machine learning methods to detect BC using mammograms [14]. The Digital Database for Mammography Screening (DDSM) [15] represents the highest generally utilized databases of the public mammogram. Several papers utilized the traditional techniques of automatic, not manual, extraction of features, including fractional Fourier transform, Gray Level Co-Occurrence Matrix (GLCM), and Gabor filter, in order to secure features, followed by applying SVM or further classifiers to conduct the classification [16; 17]. Furthermore, neural networks were utilized as classifiers [18; 19]. Recently, several papers have employed CNN for feature generations, using mammograms [20; 21]. Some authors utilized pre-trained CNN as transfer learning uses. [PERSON] et al. [22] surpassed human performance in the classification of DDSM images using CNN, exploiting transfer learning on pretrained models such as AlexNet, the ImageNet Large Scale Visual Recognition Challenge's winning network in 2012 (ILSVRC), and GoogLeNet, which won the 2014 edition of the same competition [23; 24]. [PERSON] [25] only used one Convolutional CNN, with the front convolutional layers being responsible for feature generation and the back fully connected (FC) layers acting as the classifier. Therefore, our CNN uses mammographic images as the input, and the (predicted) label as the output. With no evident overfitting, the average validation accuracy for abnormal vs. normal cases converged at around 0.905. In 2018, [PERSON] et al. used VGGNet, the winner of the ImageNet challenge in 2014, to achieve a 92.53% classification accuracy [26; 27]. The same authors exploited ResNet to localize the abnormalities within the full mammography images [28]. Recently, [PERSON] et al. extracted ROIs from mammography, both manually and with threshold-based techniques, then classified them using AlexNet chained with SVM [29]. On the CBIS DDSM dataset, they claimed an accuracy of 87.2% with a 0.94 AUC. [PERSON] et al. further extended these studies by comparing the findings of several state-of-the-art architectures; when averaging the top four models, they were able to obtain a 0.91 AUC [30]. In 2020, an important article was published in Nature [31], in which the authors trained an ensemble of three models on more than 28,000 mammogram images. Then, they compared its predictions with the decisions of radiologists. The actual labels were determined using follow-up exams or biopsies. It turns out that AI beats humans in terms of sensitivity and specificity. Some scholars have addressed the scarcity of images in the DDSM dataset by proposing data augmentation techniques. [PERSON] et al. [32] compared different transformations, proving that using augmentation functions that preserve a high amount of information (i.e., not too disruptive) helps to increase the classification accuracy. Similar results were obtained by [PERSON] et al. in a less extensive study [33]. In this study, we aim to perform abnormality classification in mammography using CNNs. The dataset of interest is the CBIS DDSM.