Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in th...
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Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in this paper, an improved strategy based on the adamalgorithm is proposed, which aims to alleviate the influence of local optimal solutions, overfitting, and slow convergence rates by controlling the restart strategy and the gradient norm joint clipping technique. This improved algorithm is abbreviated as the cg-adam algorithm. The control restart strategy performs a restart operation by periodically checking the number of steps and once the number of steps reaches a preset restart period. After the restart is completed, the algorithm will restart the optimization process. It helps the algorithm avoid falling into the local optimum and maintain convergence stability. Meanwhile, gradient norm joint clipping combines both gradient clipping and norm clipping techniques, which can avoid gradient explosion and gradient vanishing problems and help accelerate the convergence of the optimization process by restricting the gradient and norm to a suitable range. In order to verify the effectiveness of the cg-adam algorithm, experimental validation is carried out on the MNIST, CIFAR10, and Stomach datasets and compared with the adamalgorithm as well as the current popular optimization algorithms. The experimental results demonstrate that the improved algorithm proposed in this paper achieves an accuracy of 98.59%, 70.7%, and 73.2% on the MNIST, CIFAR10, and Stomach datasets, respectively, surpassing the adamalgorithm. The experimental results not only prove the significant effect of the cg-adam algorithm in accelerating the model convergence and improving generalization performance but also demonstrate its wide potential and practical application value in the field of medical image recognition.
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