白内障,以晶状体混浊而闻名,是视力障碍的常见原因,坚持作为视力丧失和失明的主要原因,提出了显著的诊断和预后挑战。这项工作提出了一个名为白内障状态检测网络(CSDNet)的新框架,它利用深度学习方法来改善白内障状态的检测。目的是创建一个框架,该框架更轻量且适用于内存或存储容量有限的环境或设备。这涉及减少可训练参数的数量,同时仍然允许从数据中有效地学习表示。此外,该框架适用于实时或接近实时的应用程序,其中快速推理是必不可少的。这项研究利用了来自眼部疾病智能识别(ODIR)数据库的白内障和正常图像。建议的模型采用较小的内核,更少的训练参数,和层,以有效地减少可训练参数的数量,与VGG19、ResNet50、DenseNet201、MIRNet、盗梦空间V3,Xception,和高效净B0。实验结果表明,该方法实现了97.24%的二元分类准确率(正常或白内障)和98.17%的平均白内障状态检测准确率(正常,1级-轻微混浊,2级未成熟白内障,3级成熟白内障,和4级-超成熟白内障),与最先进的白内障检测方法竞争。由此产生的模型重量轻,为17MB,可训练参数较少(175、617),使其适合部署在内存或存储容量受限的环境或设备中。运行时间为212ms,它非常适合需要快速推理的实时或近实时应用程序。
Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1-minimal cloudiness, grade 2-immature cataract, grade 3-mature cataract, and grade 4-hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.