Review — An Efficient Deep Neural Network Based Abnormality Detection and Multi‑Class Breast Tumor Classification

BUS-CNN, VGG-19, and YOLOv3, are Evaluated

Breast Tumor Classification Datasets
  • Breast ultrasound image datasets are trained on 3 deep-learning architectures: BUS-CNN, VGG-19, and YOLOv3.
  • YOLOv3 is found to be the best one.


  1. Three Deep-Learning Architectures
  2. 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)

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

  • VGG-19 is evaluated. (Please feel free to read VGG-19 if interested.)

1.4. YOLOv3

  • YOLOv3 is evaluated. (Please feel free to read YOLOv3 if interested.)

2. Results

Receiver Operating Characteristics (ROC) of the trained deep learning models
  • YOLOv3 obtains the best ROC curve.
Analysis of different CNN models
  • YOLOv3 obtains the highest accuracy.



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