A Cascaded Feature Extraction for Diagnosis of Ovarian Cancer in CT Images

This paper proposed ovarian cancer detection in the ovarian image using joint feature extraction and an efficient Net model. The noise of the input image is filtered by using Improved NLM (Improved Non-Local Means) filtering. The deep features are extracted using Deep CNN_RSO (Deep Convolutional Neural Network Rat Swarm Optimization), and the low-level texture features are extracted using ILBP (Interpolated Local Binary Pattern or Interpolated LBP). To improve the feature extraction and reduce the error, use a cascading technique for the feature extraction. RSO also helps to efficiently optimize the DCNN features from the images. Finally, the extracted image is classified using the Efficient Net classifier, which performs a global average summary and classification of ovarian cancer (normal and abnormal). The system’s performance is implemented on the Cancer Genome Atlas Ovarian Cancer (TCGA-OV) dataset. The system’s performance, like sensitivity, specificity, accuracy and error rates, shows better with respect to other techniques.

@article{B2022,
  title     = {A Cascaded Feature Extraction for Diagnosis of Ovarian Cancer in CT Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Arathi B and Shanthini A},
  doi       = {10.14569/IJACSA.2022.0131235},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131235}
}