The manual diagnosis of diabetic retinopathy (DR) is often invasive, time-consuming, expensive, and prone to human error. Additionally, it can be subjective, depending on the clinician’s professional experience. Recently, automated computer-aided diagnosis (CAD) systems have significantly reduced the time and effort required for diagnosis while achieving superior performance compared to traditional methods. Researchers have extensively explored deep learning (DL) and convolutional neural networks (CNNs) for diagnosing DR from fundus images, yielding promising results and offering a viable alternative to conventional diagnostic approaches. In this study, a hybrid model named EffNet-SVM is proposed for the classification of DR and no DR cases using retinal fundus images. The model is trained and tested using the Asia Pacific Tele-Ophthalmology Society (APTOS) dataset, which includes both DR and no DR images. The EffNet-SVM utilizes EfficientNetV2-Small for feature extraction from input fundus images, and the extracted features are then classified using a support vector machine (SVM) with a radial basis function (RBF) kernel. The EffNet-SVM model outperformed eight state-of-the-art DL models from the literature, achieving the highest accuracy of 97.26%. Performance metrics validate that the proposed hybrid model can be effectively integrated into CAD systems for the automated analysis of fundus images.

Retinal Fluids (fluid collections) develop because of the accumulation of fluid in the retina, which may be caused by several retinal disorders, and can lead to loss of vision. Optical coherence tomography (OCT) provides non-invasive cross-sectional images of the retina and enables the visualization of different retinal abnormalities. The identification and segmentation of retinal cysts from OCT scans is gaining immense attention since the manual analysis of OCT data is time consuming and requires an experienced ophthalmologist. Identification and categorization of the retinal cysts aids in establishing the pathophysiology of various retinal diseases, such as macular edema, diabetic macular edema, and age-related macular degeneration. Hence, an automatic algorithm for the segmentation and detection of retinal cysts would be of great value to the ophthalmologists. In this study, we have proposed a convolutional neural network-based deep ensemble architecture that can segment the three different types of retinal cysts from the retinal OCT images. The quantitative and qualitative performance of the model was evaluated using the publicly available RETOUCH challenge dataset. The proposed model outperformed the state-of-the-art methods, with an overall improvement of 1.8%.

The escalating crisis of marine pollution, especially the buildup of underwater waste, poses a significant threat to ocean ecosystems and marine life. Identifying and removing submerged debris is challenging due to poor visibility, complex water conditions, and the inefficiency of traditional manual approaches. This study leverages deep learning-based object detection techniques to improve underwater waste identification, focusing on the YOLOv8 and YOLOv9 architectures. Using the TrashCan 1.0 dataset—a large collection of labeled underwater images—the models were trained and evaluated based on key metrics such as mean Average Precision (mAP) and Intersection over Union (IoU). Results show that the YOLOv9c model performed best on the 3-Class dataset, achieving a mAP50:95 of 0.759, a mAP50 of 0.934, precision of 0.915, and recall of 0.877, highlighting its high accuracy and robustness. Meanwhile, YOLOv8l showed slightly better results on the 4-Class dataset with a mAP50:95 of 0.725, indicating dataset-dependent performance variations. The ability of these models to deliver real-time detection makes them ideal for integration into autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), enabling large-scale monitoring and cleanup operations. By advancing AI-driven solutions for marine conservation, this research contributes to more efficient and scalable efforts in mitigating the long-term impact of ocean pollution.