Abstract

Info

Title:: Deep learning-based feature extraction and discrimination of benign and malignant tumors in breast ultrasound images

Author(s):: DAI Nina; ZHANG Wenjun; Department of Ultrasound Medicine, Taihe Hospital, Affiliated Hospital of Hubei University of Medicine, Shiyan 442000, China

Keywords:: Keywords: breast ultrasound image convolutional neural network transfer learning feature extraction

Abstract:: Abstract: Considering the complexity and diversity of breast ultrasound images, a deep learning model combining convolutional neural network and transfer learning is proposed to enhance the accuracy and efficiency of tumor discrimination. Specifically, a pre-trained convolutional neural network model is utilized to extract features from breast ultrasound images, and transfer learning is employed to adapt the pre-trained model to the breast ultrasound image dataset. The extracted features are then applied to multiple classification models to differentiate between benign and malignant tumors. Experimental results demonstrate that compared with traditional image processing and machine learning methods, the proposed deep learning model exhibits significant improvements in sensitivity, specificity, and precision. The deep learning-based feature extraction method for breast ultrasound images substantially enhances the accuracy and efficiency of discrimination between benign and malignant tumors, providing an effective technical tool for the early diagnosis and treatment of breast cancer and assisting clinicians in making more accurate clinical decisions.