Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model

Cancer is a major origin of mortality around the globe, responsible for roughly high morbidity and mortality in 2020, or almost one per six deaths. Cervical, lung, and breast are the most common types of cancers. Cervical is the fourth highest common in women worldwide. Cervical would then kill approximately 4,280 women. Infections that cause, such as human papillomavirus (HPV) and hepatitis, account for approximately 30% of cases in low- and lower-middle-income countries. Many cancers are curable if detected as early as possible. In this proposed work, developed the DA-Deep convnets model (Data augmentation with a deep, Convolutional Neural Network) for the detection of cervical cancer from biopsy images. Deep Convolutional Neural Network presents one of the most applied DL approaches in medical imaging. Today, enhancements in image analysis and processing, particularly medical imaging, have become a major factor in the improvement of various systems in areas such as medical prognosis, treatment, and diagnosis. Based on our proposed model we achieved 99.2% accuracy in detecting the input image has cancer or not.

@article{Abinaya2022,
  title     = {Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {K. Abinaya and B. Sivakumar},
  doi       = {10.14569/IJACSA.2022.0131169},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131169}
}