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 5th 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.
Highlights
IntroductionAccording to the World Health Organization, breast cancer is the most prevalent globally; in 2020, about 2.3 million women were diagnosed with breast cancer
We aim to obtain a classifier to apply it in a computer-aided diagnosis (CAD) system that can help in the early detection of breast cancer, reducing the mortality rate and unnecessary examinations and expenses
We evaluated the classification efficiency of the convolutional neural networks (CNNs), with various performance metrics based on the classification performed on the set of tests and its confusion matrix
Summary
According to the World Health Organization, breast cancer is the most prevalent globally; in 2020, about 2.3 million women were diagnosed with breast cancer. At the end of 2020, there were 7.8 million women alive who were diagnosed with breast cancer [2]. 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. 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].
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