Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.

Glaucoma is a chronic eye disease that damages the optic nerve, often leading to permanent vision loss. Early screening with automated technology is crucial to assist ophthalmologists in making accurate diagnoses. One of the key technologies for automated diagnosis is the segmentation of the optic disc (OD) and optic cup (OC). In this paper, RMHA-Net, which is a developed residual multiscale feature extractor with a hybrid attention mechanism is introduced for automated OD and OC segmentation. This network’s encoder is designed based on advanced convolutional neural network (CNN) blocks that combine dilated convolution, which allows field-of-view expansion by capturing larger-scale features. In addition, the encoder also embeds residual connections to improve the model capacity in extracting low-level features. This design accurately separates the OD and OC from surrounding retinal tissues, handling complex environmental and anatomical changes. The proposed network is further improved by integrating two modules to enhance the segmentation performance: 1) a multiscale feature extractor module to provide various scales contextual information, and 2) dual attention mechanisms through channel-wise and spatial-wise mechanisms so that irrelevant information or noise can be excluded by assigning lesser weights to irrelevant features. To validate RMHA-Net’s effectiveness, extensive experiments were conducted using five public datasets: Drishti-GS, ORIGA, PAPILA, Chaksu, and REFUGE, and one private dataset, Ibn Al-Haitham. The model outperformed seven cutting-edge segmentation models for OD and OC segmentation. The results demonstrate that the network extracts detailed features, offering an efficient framework for future studies.

The accuracy of the classification process always suffers from the high dimensionality problem due to the independent, irrelevant, redundant and not useful attributes of the dataset. In this research, feature selection techniques (wrapper selection method, and information gain method) are obtained to handle the mentioned problem by removing those features and reducing the dataset dimensions. The techniques include wrapper selection method and information gain method. This research predicates on the diabetes dataset in WEKA application, which contains checking seven models enforce wrapper selection method as an attribute evaluator, forwarding direction, backward, and bi-directional best-first search method and Naïve Bayes technique as a classifier method, checking eight models applying information gain method as attribute evaluator, as well as the ranker as a search method. Additional to demonstrate the decision tree and classification figures for the best-obtained models in each one technique. The results proved the ability of wrapper and information gain to choose a minimum number of features in order to classify the data with an accuracy of more than 76% in this work.

Digital watermarking is widely used to protect medical images in terms of ownership, authenticity, and traceability; however, the embedding process may introduce subtle modifications that can affect the reliability of deep-learning-based clinical analysis. Existing studies have shown that watermarking has a negligible effect on medical image classification; nevertheless, its impact on segmentation performance remains insufficiently explored. Therefore, this paper aims to investigate the effects of segmentation model enhancement on watermarked medical image analysis. In this context, three representative watermarking approaches were employed, and five baseline segmentation models, namely U-Net, ResUNet++, SegNet, FCDenseNet, and TernausNet, were evaluated on two benchmark datasets: LIDC-IDRI and BRISC. Additionally, a novel deep learning model with nested attention mechanisms was specifically designed to improve feature extraction and increase sensitivity to subtle pixel-level variations in watermarked images. Segmentation performance was assessed using five standard evaluation metrics, including mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and the 95th percentile Hausdorff Distance (HD95). The experimental results indicate consistently minor performance degradation across both datasets. For the BRISC dataset, the reduction in mIoU ranges from 0.15% to 0.44%, while for the LIDC-IDRI dataset, it ranges from 0.19% to 0.29% compared with the no-watermarking baseline. These findings provide quantitative insight into the compatibility of watermarking techniques for medical image protection with AI-based medical image segmentation systems, highlighting their potential for broader clinical application.

Nowadays, much research attention is focused on humancomputer interaction (HCI), specifically in terms of biosignal, which has been recently used for the remote controlling to offer benefits especially for disabled people or protecting against contagions, such as coronavirus. In this paper, a biosignal type, namely, facial emotional signal, is proposed to control electronic devices remotely via emotional vision recognition. The objective is converting only two facial emotions: a smiling or nonsmiling vision signal captured by the camera into a remote control signal. The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. In terms of the smiling recognition, GENKl-4K database is exploited to train a model, which is built in the following sequenced steps: real-time video, snapshot image, preprocessing, face detection, feature extraction using HOG, and then finally SVM for the classification. The achieved recognition rate is up to 89 for the training and testing with 10-fold validation of SVM. In terms of IoT, the Arduino and MCU (Tx and Rx) nodes are exploited for transferring the resulting biosignal remotely as a server and client via the HTTP protocol. Promising experimental results are achieved by conducting experiments on 40 individuals who participated in controlling their emotional biosignals on several devices such as closing and opening a door and also turning the alarm on or off through Wi-Fi. The system implementing this research is developed in Matlab. It connects a webcam to Arduino and a MCU node as an embedded system.

An Environmental Control Unit (ECU) is an electronic device that helps a person to perform daily tasks and control the surrounding environment. Hand gesture can be employed to perform this purpose via special wearable gloves. In this paper, we propose a low-cost Human-Computer Interaction (HCI) method using Node MCU platform. Finger and palm movement is captured based on the output of a flex and accelerometer sensors. The output voltage of the flex sensor represents the degree of a finger's bending while the output of the accelerometer determines the 3-axis of palm movement in order to operate up to three devices. This glove is connected to an Internet-of-Thing (IoT) platform that supports wireless communication through a web server in order to make the ECU wire-free and hence increase the portability of the system. Comparisons with the related work confirm that the proposed system is simple, accurate and easy to implement.