# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
###### Abstract  
Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5 th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer. p 2021 11 11398  
Geography [5], for the year 2017, 24 out of 100 hospital discharges were due to malignant breast cancer tumors in the population aged 20 years or more. In 2018, 7257 female deaths caused by breast cancer were registered. In 2019, for every 100,000 women aged 20 years or older, 35.24 new breast cancer cases were reported. At the national level, the mortality rate from breast cancer is 17.19 deaths per 100,000 women aged 20 years or older. In 2020, 29,929 new cases were diagnosed, making breast cancer the disease with the first place of incidence, registering 7931 deaths with a cumulative risk of 1.18 mortality [6]. Most developed health care systems have implemented imaging for breast cancer detection based on the evidence that mammography confers a reduction in mortality [7]. The European Commission on Breast Cancer Screening and diagnosis (ECIBC) conducted a study on the optimal number of readings to allow mammography readers to work on mammography screening programs; they conclude that the increasing number of readings is related to a decrease over false positive rate of 1.35% for fewer than 3500 inspections per year; likewise, the sensitivity is increased between 0.893 and 0.939 when carrying out approximately 7000 readings; as these numbers of assessments are exceeded annually, the rates above begin to decrease [8]. Some studies on the human efficiency to detect breast cancer show that there is an area under the curve (AUC) around 82%, a sensitivity lower than 86%, a false positive detection rate of around 9.1% [9; 10]. Another study maintains that the frequency of false positives varies among readers and screening programs and has been reported in the range of 3 to 12% of screening examinations [11]. Human interpretation can also misinterpret a mammogram; about 15% to 35% of cancers that occur in screened women are not detected on imaging and are presented clinically as interval cancers due to errors or because the tumor is not perceptible to the radiologist [12]. The most surprising fact from crowdsourcing studies in the medical domain is the conclusion that many non-professional and inexperienced users as well as medical experts can perform radiological imaging [13].