Classifying Three Stages of Cataract Disease using CNN

مقدمة:   من بين العديد من الأمراض التي تصيب شبكية العين هو الساد . يعتبر مرض الساد من أخطر مشاكل الصحة العامة الدوائية في الدول النامية.  يمكن أن يحدث  دون التسبب في أي أعراض. وهو يعتبر أحد الأسباب الرئيسية للعمى أو عدم وضوح الرؤية لكبار السن. لذلك ، فإن الاكتشاف الدقيق والمبكر لإعتام عدسة العين حسب شدة الحالة مطلوب للحفاظ على الرؤية ومنع الزيادة العالمية في العمى الناجم عن إعتام عدسة العين. كما هو الحال مع معظم الأمراض المتعلقة بالعيون، فقد ثبت أن العلاجات والتشخيص المبكر يمنعان فقدان البصر . و بالمقارنة مع طرق التشخيص اليدوية، تساعد أنظمة تحليل الشبكية الأوتوماتيكي في تقليل وقت للمرضى وتقليل التكلفة. اكتسبت طرق الكشف عن مرض عتمة العين المبنية على استخدام الذكاء الاصطناعي اهتمامًا كبيرًا في المجتمع العلمي. ينتج هذا البحث طريقة فعالة وقوية للتشخيص التلقائي لإعتام عدسة العين باستخدام الشبكة العصبية التلافيفية (CNN) لاكتشاف وتصنيف الساد تلقائيًا في صور قاع العين. يستخدم محسن آدم ومجموعة بيانات (ODIR) لتدريب النموذج. تتفوق الطريقة المقترحة على أحدث أنظمة الكشف عن المياه البيضاء بمتوسط ​​دقة 100٪ لفئتين (عادي، إعتام عدسة العين)، 96.9٪ لأربع فئات (عادي، خفيف، معتدل، شديد) وفقًا للنتائج التجريبية. طرق العمل: باستخدام شبكة Convolution العصبية (CNN) لاكتشاف وتصنيف إعتام عدسة العين تلقائيًا في صور قاع العين.. الاستنتاجات:    حيث يقترح هذا البحث نظام التشخيص الآلي لإعتام عدسة العين باستخدام الشبكة العصبية العميقة (DCNN). تمت معالجة مجموعة بيانات الساد لصور قاع العين مسبقًا وتحسينها لجعل مجموعة البيانات أكثر ملاءمة لتغذية الشبكة العميقة في البداية. تعمل الشبكة المقترحة في طبقات مختلفة، ودوال التنشيط ، ودوال الخسارة، وخوارزميات التحسين من أجل تقليل تكاليف الحوسبة مع الحفاظ على دقة النموذج. استخدم النظام المقترح طرق تكبير متعددة للصور ، ثم طبق النظام على هذه الصور المعززة لتقليل مشكلة فرط التجهيز وتحسين كفاءة النظام المقترح ، حيث تم الحصول على أفضل دقة لتصنيف 96.9 بالمائة لصور قاع العين التي تمت زيادتها قاعدة بيانات ODIR ، ولكن بنسبة 94 في المائة فقط عند تطبيق النظام على صور قاع العين الأصلية. عند مقارنته بأعمال أخرى مماثلة، كان أداء هذا النظام رائعًا. نظرًا لأن هذا النهج كان فعالًا للغاية من حيث التكلفة وتوفير الوقت اللازم لطبيب العيون، فقد كان فعالاً من حيث الوقت، قادراً على اكتشاف إعتام عدسة العين بشكل أسرع ودقيق مع عدد أقل من المعاملات المستخدمة في الشبكة وطاقة كمبيوتر أقل. كذلك في صور قاع الشبكية، فإن الطريقة المقترحة قادرة على اكتشاف مراحل الساد. وتم الكشف عن مراحل إعتام عدسة العين (خفيفة، معتدلة، وشديدة) بواسطة نظام DCNNs المقترح.      Among the many diseases that affect the retina, a cataract. It is one of the most serious pharmacological public health issues in developing nations, it can develop without causing any symptoms. It is one of the prime reasons for blindness or blurred vision for senior citizens. Therefore, accurate and early detection of cataracts depending on the severity of the condition is required to preserve vision and prevent the global increase in blindness caused by cataracts. As with most of the diseases related to the eyes, treatments, and early diagnosis have been shown to prevent visual loss and blindness. Compared with the manual diagnostic methods, automated retinal analysis systems help save patients' time, vision and cost. Artificial intelligence-based cataract detection methods have gained a lot of attention in the scientific community. This research produces an efficient and robust method for the automatic diagnosis of cataract by using Convolution Neural Network (CNN) for detection and classification cataract grading automatically in fundus images. It used Adam optimizer and (ODIR) dataset to train the model. The suggested method beats state-of-the-art cataract detection systems with an average accuracy of 100 % for two classes (Normal, Cataract) ,96.9% for four classes (Normal, Mild, Moderate, Sever) according to experimental results.  Materials and Methods: Used Convolution Neural Network for detection and classification cataract grading automatically in fundus images.  Results: The suggested method beats state-of-the-art cataract detection systems with an average accuracy of 100 %  for two classes (Normal, Cataract) ,96.9% for four classes (Normal, Mild, Moderate, Sever) according to experimental results. Conclusion: The proposed network looked at different layers, activation functions, loss functions, and optimization algorithms in order to reduce computing costs while maintaining model accuracy. The proposed system used multi-image augmentation methods, then implemented the system on these augmented images to decrease the issue of overfitting and to improve the efficiency of the suggested system, as best accuracy obtained for classification 96.9 percent was get for fundus images which augmented of ODIR dataset, but only 94 percent when the system was applied to the original fundus images. When compared to other similar works, this system performed admirably. Because this approach was extremely cost- effective, accurate, and ophthalmologists, time-efficient were able to detect cataract more quickly and accuracy with fewer parameters and less computer power.  In retinal fundus images, the suggested approach is able to detect cataract phases. The detection of cataract stages (mild, moderate, and severe) will be done by the DCNNs system

A Review of the Latest Machine Learning Advances in Cataract Diagnosis

Cataract disorder is one of the most common vision disorders in the world. As the average age of the world population increases, many people suffer from it in middle and old age. Timely diagnosis can prevent the reduction of vision and eventually loss of sight. Considering the prevalence of Artificial Intelligence algorithms, especially in the medical industry, they could be used for Cataract diagnosis, IOL determination, and PCO diagnosis. According to the studies, the proposed models for Cataract diagnosis are very accurate. These developed algorithms have been able to make access to ophthalmology services easier and reduce treatment costs significantly

An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed scheme’s accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively

Accelerating precision ophthalmology: recent advances

Introduction: The future of ophthalmology is precision medicine. With a growing incidence of lifestyle-associated ophthalmic disease such as diabetic retinopathy, the use of technology has the potential to overcome the burden on clinical specialists. Advances in precision medicine will help improve diagnosis and better triage those with higher clinical need to the appropriate experts, as well as providing a more tailored approach to treatment that could help transform patient management. Areas covered: A detailed literature review was conducted using OVID Medline and PubMed databases to explore advances in precision medicine within the areas of retinal disease, glaucoma, cornea, cataracts and uveitis. Over the last three years [2019–2022] are explored, particularly discussing technological and genomic advances in screening, diagnosis, and management within these fields. Expert opinion: Artificial intelligence and its subspecialty deep learning provide the most substantial ways in which diagnosis and management of ocular diseases can be further developed within the advancing field of precision medicine. Future challenges include optimal training sets for algorithms and further developing pharmacogenetics in more specialized areas

Accelerating precision ophthalmology: recent advances

Introduction The future of ophthalmology is precision medicine. With a growing incidence of lifestyle-associated ophthalmic disease such as diabetic retinopathy, the use of technology has the potential to overcome the burden on clinical specialists. Advances in precision medicine will help improve diagnosis and better triage those with higher clinical need to the appropriate experts, as well as providing a more tailored approach to treatment that could help transform patient management. Areas covered A detailed literature review was conducted using OVID Medline and PubMed databases to explore advances in precision medicine within the areas of retinal disease, glaucoma, cornea, cataracts and uveitis. Over the last three years [2019 – 2022] are explored, particularly discussing technological and genomic advances in screening, diagnosis, and management within these fields. Expert opinion Artificial intelligence and its subspecialty deep learning provide the most substantial ways in which diagnosis and management of ocular diseases can be further developed within the advancing field of precision medicine. Future challenges include optimal training sets for algorithms and further developing pharmacogenetics in more specialized areas

Effectiveness of Machine Learning Classifiers for Cataract Screening

Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to diagnostic eye camps in rural Nepal compared to traditional diagnostic eye camps. There was a total of 22,805 patients included in the decision tree, and the ML-based eye camp was able to identify 31 additional cases of cataracts, and 2546 additional cases of non-cataract