Hnoohom N.Maitrichit N.Wongpatikaseree K.Yuenyong S.Mekruksavanich S.Jitpattanakul A.Mahidol University2023-06-182023-06-182022-01-01Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 148-152https://repository.li.mahidol.ac.th/handle/20.500.14594/84338This paper presents an efficient approach to generate visual explanations from a ResNet 101 model for identification of medication. The blister package dataset is used to train a deep learning model built on the PyTorch framework's ResNet 101 pre-trained model. Visual inspections and a quantitative localization benchmark demonstrate that the model approach correctly identifies the critical components of blister packs for medicine identification. A Gradient-weighted Class Activation Mapping (Grad-CAM) method is used to extract the feature map, and then the attention mechanism is utilized to extract the high-level attention maps, that emphasizes the part of the image that is significant to the target class, which can be considered as a visual representation. The experimental results indicated that the Grad-CAM achieved the better visualization and interpretation of ResNet 101 in blister package classification.Computer ScienceConference PaperSCOPUS10.1109/RI2C56397.2022.99103172-s2.0-85141810183