A New Model for Breast Cancer Classification Using VGGNet

Published on November 4, 2022

Breast cancer, like a formidable opponent in a game of chess, poses a significant challenge to doctors and researchers. Detecting and classifying this disease requires advanced computational methods. Various techniques have been used, such as k-nearest neighbor (KNN) and support vector machine (SVM), but they each come with their limitations. In this study, a new model based on the Visual Geometry Group network (VGGNet) was proposed. The VGGNet-12 model, with reduced layers compared to its predecessor VGGNet-16, addressed the issue of overfitting in breast cancer classification. The performance of this model was compared to CNN and LeNet models, and it proved to be an improvement in terms of accuracy and reliability. These results offer a promising step forward in the field of breast cancer diagnosis and classification. Excited to learn more? Dive into the full research article for a deeper understanding of this groundbreaking study!

Cancer is one of the most prevalent diseases worldwide. The most prevalent condition in women when aberrant cells develop out of control is breast cancer. Breast cancer detection and classification are exceedingly difficult tasks. As a result, several computational techniques, including k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and genetic algorithms, have been applied in the current computing world for the diagnosis and classification of breast cancer. However, each method has its own limitations to how accurately it can be utilized. A novel convolutional neural network (CNN) model based on the Visual Geometry Group network (VGGNet) was also suggested in this study. The 16 layers in the current VGGNet-16 model lead to overfitting on the training and test data. We, thus, propose the VGGNet-12 model for breast cancer classification. The VGGNet-16 model has the problem of overfitting the breast cancer classification dataset. Based on the overfitting issues in the existing model, this research reduced the number of different layers in the VGGNet-16 model to solve the overfitting problem in this model. Because various models of the VGGNet, such as VGGNet-13 and VGGNet-19, were developed, this study proposed a new version of the VGGNet model, that is, the VGGNet-12 model. The performance of this model is checked using the breast cancer dataset, as compared to the CNN and LeNet models. From the simulation result, it can be seen that the proposed VGGNet-12 model enhances the simulation result as compared to the model used in this study. Overall, the experimental findings indicate that the suggested VGGNet-12 model did well in classifying breast cancer in terms of several characteristics.

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