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- W4312305455 abstract "This 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." @default.
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- W4312305455 date "2022-08-04" @default.
- W4312305455 modified "2023-09-29" @default.
- W4312305455 title "Visual Explanations of ResNet 101 for Blister Package Classification" @default.
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- W4312305455 doi "https://doi.org/10.1109/ri2c56397.2022.9910317" @default.
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