Standardized Transfer Learning Models Enhance Classification of Breast Ultrasound Data
- Resource Type
- Conference
- Authors
- Bota, Maria Aurora; Ciobotaru, Alexandru; Bota, Peter; Gota, Dan Ioan; Stefan, Iulia Adina; Valean, Honoriu; Miclea, Liviu
- Source
- 2023 27th International Conference on System Theory, Control and Computing (ICSTCC) System Theory, Control and Computing (ICSTCC), 2023 27th International Conference on. :434-439 Oct, 2023
- Subject
- Computing and Processing
Robotics and Control Systems
Training
Solid modeling
Ultrasonic imaging
Transfer learning
Breast
Feature extraction
Prediction algorithms
Breast Ultrasound(BUS)
Convolutional Neural Network(CNN)
Deep Learning
Transfer Learning
- Language
- ISSN
- 2473-5698
Ultrasound imaging is an often employed technique in diagnosing of breast cancer, although the prediction reliability depends on the specialist’s experience. Computer Aided Diagnosis (CAD) systems have been introduced for the enhancement the quality and time invested in classifying breast ultrasound(BUS) images. Deep Convolutional Neural Networks based algorithms is considered one of the most successful strategy in breast ultrasound image analysis. Data limitation is one of the prioritizing issues at the current moment. This problem is referred by introducing transfer-learning-based models and stratification as a data augmentation technique for achieving a better accuracy of the classification. The paper has demonstrated that the deep feature extraction and feature selection can categorized the breast ultrasound images using the pre-training methods. A dataset containing 1578 breast ultrasound images was used for model training and testing, and the optimal level of achievement was reached by InceptionResNetV2 and DenseNet121 with an accuracy of 83% and an ”one over the rest” AUC score of 0.933 for DenseNet121, respectively 0.923 for InceptionResNetV2.