Review — An Efficient Deep Neural Network Based Abnormality Detection and Multi‑Class Breast Tumor Classification
An Efficient Deep Neural Network Based Abnormality Detection and Multi‑Class Breast Tumor Classification, BUS-CNN, by Dr. A.P.J.Abdul Kalam Technical University, 2022 JMTA (Sik-Ho Tsang @ Medium)
Medical Imaging, Medical Image Analysis, Image Classification
- Three Deep-Learning Architectures
1. Three Deep-Learning Architectures
- Standard deep learning workflow is presented.
- The dataset is augmented, labeled and annotated. Then, it is split into training/validation/testing set. Then the training and validation sets are used for training the model. The testing set is used for evaluation.
1.2. Breast Ultrasound Convolution Neural Network (BUS-CNN)
- BUS-CNN consists of six convolutional layers and three max pooling layers.
- At the end, there are fully connected layers to sense the presence or absence of the breast tumor and its classification probability.