Deep-modified transfer learning-based CNN networks for enhanced breast cancer prediction

Abstract

Breast cancer (BC) is one of the most fatal forms of cancer, making it a significant contributor to mortality rates worldwide. Early detection and timely treatment of breast cancer are crucial in reducing its mortality rate. To ensure a healthy lifestyle, it is essential to develop systems that can accurately diagnose breast cancer. Recent advances in modern computing and information technologies have enabled significant progress in the early detection and prediction of diseases within healthcare systems. This study proposes a method for precise and automatic breast cancer prediction using deep-modified transfer learning-based Convolutional Neural Networks (CNNs). The CNN architectures employed include ResNet50, MobileNetV2, DenseNet121, and Xception, which serve as feature extractors to capture the most relevant features of breast Ultrasound images (BUSI). These extracted features are then accurately classified as benign or malignant using various high-performance classifiers, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Softmax. The experimental results demonstrate that the proposed deep modified DenseNet121 network with the Softmax classifier outperformed other models and existing techniques. This latter achieved remarkable performance metrics, including an accuracy of 95.34%, a precision of 90.90%, and an F1 score of 93.02%. These results highlight the effectiveness of our approach in enhancing the accuracy of breast cancer prediction. The superior performance of the proposed method provides significant improvements in decision-making speed and reduces the time, effort, and laboratory resources required for healthcare services. Consequently, this method has the potential to significantly enhance early diagnosis and enable more tailored treatment plans, ultimately contributing to better patient outcomes and reducing the overall mortality rates associated with breast cancer.