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This study presents a gastrointestinal image classification method utilizing an improved Adam algorithm, named AdamW_AGC, which integrates weight decay and Adaptive Gradient Clipping (AGC) strategies to enhance classification accuracy and training stability. Experimental results show that the proposed method achieved a classification accuracy of 75.8% on the HyperKvasir dataset, outperforming the traditional Adam algorithm by 1.6%, and demonstrated high accuracy on MNIST and CIFAR10 datasets as well. The fi

Author

ikhsanarifrahmat94

Language

EN