This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical data but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional Gray Wolf Optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. This paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. This algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting.

翻译：本文针对医学图像处理中的挑战，提出了一种创新解决方案，即将自适应曲线灰狼优化（ACGWO）算法融入神经网络反向传播过程。神经网络在医学数据处理中展现出潜力，但因数据不平衡和稀缺性而存在过拟合及可解释性不足等问题。传统的灰狼优化（GWO）算法也存在种群多样性不足和早熟收敛等缺陷。本文通过引入自适应算法，采用Sigmoid函数对标准GWO算法进行改进，以解决上述问题。该算法在六个著名测试函数上与四种前沿算法进行了广泛比较，并显著优于它们。此外，利用ACGWO算法，我们增强了神经网络的鲁棒性和泛化能力，从而获得更具可解释性的预测结果。在公开可获取的克利夫兰心脏病数据集上，本方法超越了其他十种方法，达到了86.8%的准确率，表明其在临床环境中高效预测心脏病的潜力。