SE-RESNET: Monkeypox Detection Model

The monkeypox virus, a species of the Orthopoxvirus genus within the family Poxviridae, is answerable for inflicting monkeypox. The symptoms of monkeypox last for about two to three weeks, which is often a self-limiting infection. There may be extreme cases. Recently, the case fatality rate has been in the region of 3-6. When developing a clinical medical diagnosis, it is vital to incorporate different rash diseases such as pox, measles, bacterial skin infections, scabies, syphilis, and medically connected allergies. Pathology at the symptom stage of the sickness could aid in distinctive monkeypox from chickenpox or smallpox. The dataset’s machine learning model should not be used for clinical diagnosis, but rather for developing a new model to identify illness fast. The gray scale versions of the original photos in the Monkeypox grey file could make it easier to figure out training more quickly. The channel-wise feature responses that are adaptively re-calibrated are handled by the “Squeeze-and-Excitation” (SE) block. To do this, cross-channel dependency must be explicitly modeled. To demonstrate how these architectures are put together and how these building pieces may be layered to produce SE-Resnet designs in monkeypox image sets that generalize very well. Also, demonstrate that employing SE blocks significantly enhances the performance of current state-of-the-art CNNs while incurring just a little computational cost.

@article{Thiruppathi2023,
  title     = {SE-RESNET: Monkeypox Detection Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Krishnan Thiruppathi and Selvakumar K and Vairachilai Shenbagavel},
  doi       = {10.14569/IJACSA.2023.0140959},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140959}
}