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In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substantially improve the independence and safety of visually impaired individuals in various environments. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue titled "Deep and Machine Learning for Image Processing: Medical and Non-medical Applications." The special issue explores the use of machine learning and deep learning technologies in image processing, with a focus on medical and non-medical applications. The contributions cover a wide range of topics, including the use of ensemble models for diagnosing myocardial infarction, camouflaged object detection, crime analysis using seasonal autoregressive integrated moving average models, monkeypox diagnosis using metaheuristic optimization algorithms, instance segmentation for power operation monitoring, long-distance person detection, hate-speech detection on social media, perceptual image quality prediction, material discrimination using colorization, compressed sensing magnetic resonance imaging, quantization for low-power accelerators, and computer-aided diagnosis for skin diseases. The document also highlights future research directions, such as refining integrated ML/DL models, broadening predictive modeling, enhancing data fusion, advancing imaging and material discrimination techniques, developing context-aware natural language processing models, improving object detection, optimizing for edge computing, innovating in medical imaging, and assessing multimedia content quality. The special issue aims to showcase the current state of research in ML and DL applications for image processing and inspire further advancements in the field. [Extracted from the article]