{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:26:26Z","timestamp":1789575986946,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University)"},{"name":"GUST Engineering and Applied Innovation Research Center at Gulf University for Science and Technology, Kuwait"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2014minimal cloudiness, grade 2\u2014immature cataract, grade 3\u2014mature cataract, and grade 4\u2014hyper 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.<\/jats:p>","DOI":"10.3390\/diagnostics14100983","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T13:20:24Z","timestamp":1715174424000},"page":"983","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Lahari","family":"P.L","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramesh","family":"Vaddi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud O.","family":"Elish","sequence":"additional","affiliation":[{"name":"Computer Science Department, Gulf University for Science and Technology, Hawally 32093, Kuwait"},{"name":"GUST Engineering and Applied Innovation Research Center, Gulf University for Science and Technology, Hawally 32093, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-2254","authenticated-orcid":false,"given":"Venkateswarlu","family":"Gonuguntla","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University), Pune, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siva Sankar","family":"Yellampalli","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pratap, T., and Kokil, P. 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