Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.
Keyphrases
- deep learning
- neural network
- machine learning
- magnetic resonance imaging
- healthcare
- climate change
- neoadjuvant chemotherapy
- convolutional neural network
- high resolution
- optical coherence tomography
- lymph node metastasis
- contrast enhanced ultrasound
- squamous cell carcinoma
- quality improvement
- radiation therapy
- rna seq
- young adults
- breast cancer risk
- lymph node