Huang Chunjuan, Sarabi Mohammad, Ragab Adham E
Guangling College, Yangzhou University, Yangzhou, China.
Ankara Yıldırım Beyazıt University (AYBU), 06010, Ankara, Turkey.
Heliyon. 2024 Aug 31;10(17):e37293. doi: 10.1016/j.heliyon.2024.e37293. eCollection 2024 Sep 15.
Diabetic retinopathy is a serious eye disease that may lead to loss of vision if it is not treated. Early detection is crucial in preventing further vision impairment and enabling timely interventions. Despite notable advancements in AI-based methods for detecting diabetic retinopathy, researchers are still striving to enhance the efficiency of these techniques. Therefore, obtaining an efficient technique in this field is essential. In this research, a new strategy has been proposed to improve the detection of diabetic retinopathy by increasing the accuracy of diagnosis and identifying cases in the initial stages. To achieve this, it has been proposed to integrate the MobileNet-V2 deep learning-based neural network with Improved Fire Hawk Optimizer (IFHO). The MobileNet-V2 network has been renowned for its efficiency and accuracy in image classification tasks, making it a suitable candidate for diabetic retinopathy detection. By combining it with the IFHO, the feature selection process has been optimized, which is essential for identifying relevant patterns and abnormalities related to diabetic retinopathy. The Diabetic Retinopathy 2015 dataset has been used to evaluate the effectiveness of the MobileNet-V2/IFHO model. The study results indicate that the DRMNV2/IFHO model consistently outperforms other methods in terms of precision, accuracy, and recall. Specifically, the model achieves an average precision of 97.521 %, accuracy of 96.986 %, and recall of 98.543 %. Moreover, when compared to advanced techniques, the DRMNV2/IFHO model demonstrates superior performance in specificity, F1-score, and AUC, with average values of 97.233 %, 93.8 %, and 0.927, respectively. These results underscore the potential of the DRMNV2/IFHO model as a valuable tool for improving the accuracy and efficiency of DR diagnosis. Nevertheless, additional validation and testing on larger datasets are required to verify the model's effectiveness and robustness in real-world clinical scenarios.
糖尿病性视网膜病变是一种严重的眼部疾病，如果不进行治疗可能会导致视力丧失。早期检测对于预防进一步的视力损害和及时进行干预至关重要。尽管基于人工智能的糖尿病性视网膜病变检测方法取得了显著进展，但研究人员仍在努力提高这些技术的效率。因此，在该领域获得一种高效的技术至关重要。在本研究中，提出了一种新策略，通过提高诊断准确性和在初始阶段识别病例来改进糖尿病性视网膜病变的检测。为实现这一目标，建议将基于MobileNet-V2深度学习的神经网络与改进的火鹰优化器（IFHO）相结合。MobileNet-V2网络在图像分类任务中的效率和准确性方面享有盛誉，使其成为糖尿病性视网膜病变检测的合适候选者。通过将其与IFHO相结合，优化了特征选择过程，这对于识别与糖尿病性视网膜病变相关的相关模式和异常至关重要。糖尿病性视网膜病变2015数据集已用于评估MobileNet-V2/IFHO模型的有效性。研究结果表明，DRMNV2/IFHO模型在精度、准确性和召回率方面始终优于其他方法。具体而言，该模型的平均精度为97.521%，准确性为96.986%，召回率为98.543%。此外，与先进技术相比，DRMNV2/IFHO模型在特异性、F1分数和AUC方面表现出卓越性能，平均值分别为97.233%、93.8%和0.927。这些结果强调了DRMNV2/IFHO模型作为提高糖尿病性视网膜病变诊断准确性和效率的有价值工具的潜力。然而，需要在更大的数据集上进行额外的验证和测试，以验证该模型在实际临床场景中的有效性和稳健性。
