Visual Explanations of ResNet 101 for Blister Package Classification
8
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2022-01-01
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2-s2.0-85141810183
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Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022
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148
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152
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SCOPUS
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Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 148-152
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Hnoohom N., Maitrichit N., Wongpatikaseree K., Yuenyong S., Mekruksavanich S., Jitpattanakul A. Visual Explanations of ResNet 101 for Blister Package Classification. Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 148-152. 152. doi:10.1109/RI2C56397.2022.9910317 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84338
item.page.dc.title
Visual Explanations of ResNet 101 for Blister Package Classification
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item.page.dc.description.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.