Deep belief network classification model for accurate breast cancer detection and diagnosis

Abstract

Breast cancer is still one of the common malignancies and endemics that are fatal to women across the globe. Early-stage diagnosis helps reduce the percentage of deaths because treatment outcomes are much better at that stage. As the contemporary approaches in machine learning (ML) and deep learning (DL) emerged, the automatic detection of breast cancer has received a great consideration for their ability to improve diagnosis and treatment. We present a new deep belief network (DBN) based breast cancer detection system to increase the accuracy and the dependability of the diagnosis of breast cancer. The major modules of the system are image preprocessing, feature extraction and the DBN-based classification to guarantee accurate detection and classification of malignant and benign breast lesions. We compared the proposed DBN model with the existing DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). It is with respect to critical features of the model performance which includes accuracy, precision, recall, specificity and F1-score. The methodologies used in this study show that the performance of the proposed DBN model is significantly better than these conventional algorithms in accuracy and sensitivity where the DBN model is an ideal method for the early detection of breast cancer. Through extensive experimentation, we compared the proposed DBN model with existing DL techniques such as CNNs, RNNs, LSTMs, and GANs. Our results show that the proposed DBN model outperforms these models in several key performance metrics.