Abstract：
In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces \"NIMEQ-SACNet,\" a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet\'s parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model\'s ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet\'s pre-eminence over prevailing algorithms and classification frameworks.
摘要：
在精准医学领域，深度学习的潜力被逐步利用，以促进复杂的临床决策，尤其是在导航包含Omics的多层面数据集时，临床,image,装置,社会,和环境维度。这项研究强调了图像数据的重要性，鉴于其在检测和分类威胁视力的糖尿病性视网膜病变（VTDR）中的重要作用，VTDR是导致视力障碍的主要因素。及时识别VTDR是有效干预和减轻视力丧失的关键。解决这个问题，这项研究介绍了“NIMEQ-SACNet，“一种新颖的混合模型，其强大的量子启发二进制灰狼优化器（EQI-BGWO）具有自我注意胶囊网络。所提出的方法的特点是两个关键的进步：首先，通过量子计算方法增强二进制灰狼优化，其次,部署增强的EQI-BGWO以熟练校准SACNet的参数，最终导致VTDR分类准确性的显著提升。所提出的模型处理二进制的能力，5阶段,7阶段VTDR分类非常值得注意。对眼底图像数据集的严格评估，由准确性等指标强调，灵敏度,特异性,Precision,F1-Score,和MCC,证明了NIMEQ-SACNet在主流算法和分类框架上的卓越地位。
