@proceedings{99582,
  author = {Surasinghe P and Sabapathippillai P and Thanikasalam K},
  title = {Detection and Visualization of Neglected Tropical Skin Diseases Using EfficientNet and Grad-CAM},
  abstract = {<p>Early skin disease detection is crucial for both effective treatment and the prevention of spreading to others. Neglected Tropical Skin Diseases (skin-NTDs) primarily affect low-income and developing countries, receiving inadequate attention and resources in comparison to other health issues. In this study, an automated system has been developed for detecting five skin-NTDs, capable of recognizing the diseases from raw lesion images without requiring any pre-processing. A dataset was created in the initial phase of the study since no publicly available benchmarks are available. The EfficientNet family of pre-trained models was utilized to train the classifier, and the EfficientNet-B3 was selected based on the experimental results. Additionally, the proposed work has developed a Grad-CAM based visualization technique to identify the most influential regions within images for the classification of specific diseases. The proposed model exhibited an overall classification accuracy of 91.53% on the test data. The proposed work will offer benefits to frontline medical staff and local residents in low-income countries.</p>
},
  year = {2023},
  journal = {2023 5th International Conference on Advancements in Computing (ICAC)},
  publisher = {IEEE},
  doi = {10.1109/icac60630.2023.10417550},
  language = {Eng},
}