Habeb Abduljlil Abduljlil Ali Abduljlil, Zhu Ningbo, Taresh Mundher Mohammed, Ahmed Ali Ali Talal
College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.
Research Institute, Hunan University, Chongqing, Chongqing, China.
PeerJ Comput Sci. 2024 Mar 29;10:e1923. doi: 10.7717/peerj-cs.1923. eCollection 2024.
While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces a novel optimizer that integrates the Caputo fractional gradient descent (CFGD) method with the cuckoo search algorithm (CSA) to enhance accuracy and convergence speed, seeking optimal solutions. The proposed optimizer's performance is assessed by training well-known Vgg16, AlexNet, and GoogLeNet models on 400 fundus images, equally divided between benign and malignant classes. Results demonstrate the significant potential of the proposed optimizer in improving classification accuracy and convergence speed. In particular, the mean accuracy attained by the proposed optimizer is 86.43%, 87.42%, and 87.62% for the Vgg16, AlexNet, and GoogLeNet models, respectively. The performance of our optimizer is compared with existing approaches, namely stochastic gradient descent with momentum (SGDM), adaptive momentum estimation (ADAM), the original cuckoo search algorithm (CSA), Caputo fractional gradient descent (CFGD), beetle antenna search with ADAM (BASADAM), and CSA with ADAM (CSA-ADAM). Evaluation criteria encompass accuracy, robustness, consistency, and convergence speed. Comparative results highlight significant enhancements across all metrics, showcasing the potential of deep learning techniques with the proposed optimizer for accurately identifying ocular tumors. This research contributes significantly to the development of computer-aided diagnosis systems for ocular tumors, emphasizing the benefits of the proposed optimizer in medical image classification domains.
虽然数字眼底图像常用于诊断眼部肿瘤，但由于其复杂性和肿瘤特有的细微特征，解读这些图像具有挑战性。眼部肿瘤的自动检测对于及时诊断和有效治疗至关重要。本研究调查了一种用于眼部肿瘤分类的强大深度学习系统。文章介绍了一种新颖的优化器，该优化器将Caputo分数阶梯度下降（CFGD）方法与布谷鸟搜索算法（CSA）集成，以提高准确性和收敛速度，寻求最优解。通过在400张眼底图像上训练著名的Vgg16、AlexNet和GoogLeNet模型来评估所提出优化器的性能，这些图像在良性和恶性类别之间平均分配。结果表明所提出的优化器在提高分类准确性和收敛速度方面具有巨大潜力。特别是，对于Vgg16、AlexNet和GoogLeNet模型，所提出的优化器分别达到的平均准确率为86.43%、87.42%和87.62%。将我们优化器的性能与现有方法进行比较，即带动量的随机梯度下降（SGDM）、自适应动量估计（ADAM）、原始布谷鸟搜索算法（CSA）、Caputo分数阶梯度下降（CFGD）、带ADAM的甲虫触角搜索（BASADAM）和带ADAM的CSA（CSA-ADAM）。评估标准包括准确性、鲁棒性、一致性和收敛速度。比较结果突出了在所有指标上的显著提升，展示了使用所提出的优化器的深度学习技术在准确识别眼部肿瘤方面的潜力。这项研究对眼部肿瘤计算机辅助诊断系统的发展做出了重大贡献，强调了所提出的优化器在医学图像分类领域的优势。
