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
Outline
- Three Deep-Learning Architectures
- Results
1. Three Deep-Learning Architectures
1.1. Framework
- 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.
1.3. VGG-19
1.4. YOLOv3
Reference
[2022 JMTA] [BUS-CNN]
An Efficient Deep Neural Network Based Abnormality Detection and Multi‑Class Breast Tumor Classification
4.1. Biomedical Image Classification
2017 [ChestX-ray8] 2019 [CheXpert] 2020 [VGGNet for COVID-19] [Dermatology] 2021 [CheXternal] [CheXtransfer] 2022 [BUS-CNN]