Breast Cancer Detection System using Deep Learning Based on Fusion Features and Statistical Operations

Breast cancer is considered as the second cause of death for women. The earlier is diagnosed, the easier the patients can be recovered. The need for studies to detect this kind of cancer easily and accurately came from the growing rate of infected patents by breast cancer exponentially. This study is conducted to investigate the use of deep-learning model for breast cancer detecting using the technique VGG-19 and ultrasound images. Two layers of VGG19 structure were used: (i.e. fc6 and fc7. Based on these two layers (fc6 and fc7), new datasets were created, which are named as statistical operations. These datasets will be employed as input for the following Machine Learning classifiers: K-Nearest Neighbors, Random Forest, Naïve Bayes and Decision Tree. Data augmentation was considered to increase the dataset size for better learning of CNN. Random Forest achieved high accuracy (88.63), precision (0.88), recall (0.88) and F-Measure (0.88). The results of the classification accuracy in the three scenarios are slightly similar; this proves that the breast cancer can be detected even if the size of data in the training dataset was minimal.

@article{AlShowarah2023,
  title     = {Breast Cancer Detection System using Deep Learning Based on Fusion Features and Statistical Operations},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Suleyman A. AlShowarah},
  doi       = {10.14569/IJACSA.2023.0141165},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141165}
}