Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

A transfer learning enabled approach for ocular disease detection and classification

Ocular diseases pose significant challenges in timely diagnosis and effective treatment. Deep learning has emerged as a promising technique in medical image analysis, offering potential solutions for accurately detecting and classifying ocular diseases. In this research, we propose Ocular Net, a novel deep learning model for detecting and classifying ocular diseases, including Cataracts, Diabetic, Uveitis, and Glaucoma, using a large dataset of ocular images. The study utilized an image dataset comprising 6200 images of both eyes of patients. Specifically, 70% of these images (4000 images) were allocated for model training, while the remaining 30% (2200 images) were designated for testing purposes. The dataset contains images of five categories that include four diseases, and one normal category. The proposed model uses transfer learning, average pooling layers, Clipped Relu, Leaky Relu and various other layers to accurately detect the ocular diseases from images. Our approach involves training a novel Ocular Net model on diverse ocular images and evaluating its accuracy and performance metrics for disease detection. We also employ data augmentation techniques to improve model performance and mitigate overfitting. The proposed model is tested on different training and testing ratios with varied parameters. Additionally, we compare the performance of the Ocular Net with previous methods based on various evaluation parameters, assessing its potential for enhancing the accuracy and efficiency of ocular disease diagnosis. The results demonstrate that Ocular Net achieves 98.89% accuracy and 0.12% loss value in detecting and classifying ocular diseases by outperforming existing methods.

Absorption enhancement strategies in chitosan-based nanosystems and hydrogels intended for ocular delivery: Latest advances for optimization of drug permeation

Ophthalmic diseases can be presented as acute diseases like allergies, ocular infections, etc., or chronic ones that can be manifested as a result of systemic disorders, like diabetes mellitus, thyroid, rheumatic disorders, and others. Chitosan (CS) and its derivatives have been widely investigated as nanocarriers in the delivery of drugs, genes, and many biological products. The biocompatibility and biodegradability of CS made it a good candidate for ocular delivery of many ingredients, including immunomodulating agents, antibiotics, ocular hypertension medications, etc. CS-based nanosystems have been successfully reported to modulate ocular diseases by penetrating biological ocular barriers and targeting and controlling drug release. This review provides guidance to drug delivery formulators on the most recently published strategies that can enhance drug permeation to the ocular tissues in CS-based nanosystems, thus improving therapeutic effects through enhancing drug bioavailability. This review will highlight the main ocular barriers to drug delivery observed in the nano-delivery system. In addition, the CS physicochemical properties that contribute to formulation aspects are discussed. It also categorized the permeation enhancement strategies that can be optimized in CS-based nanosystems into four aspects: CS-related physicochemical properties, formulation components, fabrication conditions, and adopting a novel delivery system like implants, inserts, etc. as described in the published literature within the last ten years. Finally, challenges encountered in CS-based nanosystems and future perspectives are mentioned.

Artificial intelligence enhanced ophthalmological screening in children: insights from a cohort study in Lubelskie Voivodeship

This study aims to investigate the prevalence of visual impairments, such as myopia, hyperopia, and astigmatism, among school-age children (7-9 years) in Lubelskie Voivodeship (Republic of Poland) and apply artificial intelligence (AI) in the detection of severe ocular diseases. A total of 1049 participants (1.7% of the total child population in the region) were examined through a combination of standardized visual acuity tests, autorefraction, and assessment of fundus images by a convolutional neural network (CNN) model. The results from this artificial intelligence (AI) model were juxtaposed with assessments conducted by two experienced ophthalmologists to gauge the model's accuracy. The results demonstrated myopia, hyperopia, and astigmatism prevalences of 3.7%, 16.9%, and 7.8%, respectively, with myopia showing a significant age-related increase and hyperopia decreasing with age. The AI model performance was evaluated using the Dice coefficient, reaching 93.3%, indicating that the CNN model was highly accurate. The study underscores the utility of AI in the early detection and diagnosis of severe ocular diseases, providing a foundation for future research to improve paediatric ophthalmic screening and treatment outcomes.

EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery

BACKGROUND

The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking.

OBJECTIVE

Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3.

METHODS

A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%.

RESULTS

After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment.

CONCLUSION

The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at ( https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/ ).

Improved detection accuracy of red lesions in retinal fundus images with superlearning approach

BACKGROUND

Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions.

METHOD

In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to base-learners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. In this work, we have handled the data imbalance problem and compared the final accuracy with different synthetic data creation ratios.

RESULT

The suggested approach uses publicly available resources to perform quantitative assessments at lesions-level. The overall accuracy of red lesion segregation is 93.5%, which has increased to 97.88% when data imbalance problem is taken care-off.

CONCLUSION

The results of our system have achieved competitive performance compared with other modern approaches and handling of data imbalance further increases the performance of it.