Abstract
Breast cancer is a malignant tumor that affects women. It is the most prevalent cancer in women, affecting about 10% of all women at any point in their lives. The development of breast cancer begins in the lobules or ducts of the cells. Early detection and prevention are the best ways to stop this cancer from spreading. In this study, five Convolution Neural Network (CNN) models are used to process image data of breast cells. AlexNet, InceptionV3, GoogLeNet, VGG19 and Xception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells. The models are trained and tested at different epochs to record the learning rate. It is observed from the study that with higher epochs, the data loss decreases and accuracy increases. The accuracy of InceptionV3 and Xception is 92.48% and 90.72% respectively. Likewise, VGG19 and AlexNet have fairly close accuracy of 94.83% and 96.74%. However, GoogLeNet dominates over the other implemented models with the highest accuracy of 97.80%. The GoogLeNet model performs with high accuracy and precision in detecting IDC cells responsible for breast cancer.
Highlights
Cancer, known as a malignant neoplasm, is a group of more than a hundred diseases marked by irregular cell development with the ability to spread to the body's underlying tissues
AlexNet, InceptionV3, GoogLeNet, VGG19 and Xception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells
The main focus of this paper is to detect breast cancer that an early stage using the images of IDC cells in the breast
Summary
Known as a malignant neoplasm, is a group of more than a hundred diseases marked by irregular cell development with the ability to spread to the body's underlying tissues. To classify and predict breast cancer, machine learning algorithms with image processing have become quite famous for their accuracy in detecting the disease at an early stage. A related study [6] used Transfer Learning in CNNs to identify and segment brain and colon cancer images, and the findings were cutting-edge. It used AlexNet (pre-trained on ImageNet) to train a Support Vector Machine with the features extracted from the last FC layer Support Vector Machine (SVM). The experimental results indicate that the SVM-RBF kernel outperforms other classifiers, scoring 96.84% accuracy in the Wisconsin Breast Cancer (original) datasets.
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