Breast Cancer Diagnosis Improvement Based Deep Learning

Background: Globally, Breast cancer is the utmost predominant cancer and it affects millions of women every year. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been familiar as an efficient modality for diagnosing breast cancer. In spite of DCE-MRI modality being majorly utilized for the classification of breast cancer, the diagnostic performance is still deficient and misclassification occurs. Method: This research proposes the Deep Learning (DL) approach of Dual Attention Deep Convolutional Neural Network (DADCNN) for the classification of breast cancer into two types named benign and malignant. Initially, the DCE-MRI, Rider Breast MRI and Breast MRI datasets are utilized for estimating the effectiveness of the classifier. After collecting the dataset, pre-processing is performed by utilizing data augmentation technique. Then, the augmented data is input for the feature extraction process, which is performed by using DenseNet-121 and ResNet-101 architectures. Then, the extracted features are concatenated by using the feature fusion model and finally, classification is performed to categorize the breast cancer. The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the results. The presented DADCNN approach significantly outperforms the existing methods like MUM-Net-joint prediction, UDFS + SVM, XGBoost, Multivariate Rocket and BI-RADS. The greater accuracy of the proposed DACNN approach suggests DL approach to effectively enhance the classification accuracy in breast cancer. Results: The experimental results establish that the proposed method attains greater results in all performance metrics as compared to the exiting methods like Multi-modality Network (MUM-Net) and Multivariate Rocket algorithm, The suggested DADCNN approach attains the maximum accuracy of 0.931, specificity of 0.924, sensitivity of 0.925, AUC of 0.962, PPV of 0.853 and NPV of 0.902 in breast cancer classification, which denotes that the DACNN effectively classify the cancer into benign and malignant. Concluding Remarks: The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the accuracy, specificity, sensitivity, AUC, PPV and NPV in breast cancer classification.