Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and soft exudates. Computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping in screening and defining the best form of treatment. However, the detection of these lesions through computerized systems is a challenge due to numerous factors, such as the characteristics of size and shape of the lesions, noise and the contrast of images available in the public datasets of Diabetic Retinopathy, the number of labeled examples of these lesions available in the datasets and the difficulty of deep learning algorithms in detecting very small objects in digital images. Thus, to overcome these problems, this work proposes a new approach based on image processing techniques, data augmentation, transfer learning, and deep neural networks to assist in the medical diagnosis of fundus lesions. The proposed approach was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets and implemented in the PyTorch framework based on the YOLOv5 model. The proposed approach reached in the DDR dataset an mAP of 0.2630 for the IoU limit of 0.5 and F1-score of 0.3485 in the validation stage, and an mAP of 0.1540 for the IoU limit of 0.5 and F1-score of 0.2521, in the test stage. The results obtained in the experiments demonstrate that the proposed approach presented superior results to works with the same purpose found in the literature.

This paper proposes an automatic classification method to detect glaucoma in fundus images. The method is based on training a neural network using public image databases. The network used in this paper is the GoogLeNet, adapted for this proposal. The methodology was divided into two stages, namely: (1) detection of the region of interest (ROI); (2) image classification. We first used a sliding-window approach combined with the GoogLeNet network. This network was trained using manually extracted ROIs and other fundus image structures. Afterwards, another GoogLeNet model was trained using the previous resulting images. Then those images were used to train another GoogLeNet model to automatically detect glaucoma. To prevent overfitting, data augmentation techniques were used on smaller databases. The results demonstrated that the network had a good accuracy, even with poor quality images found in some databases or generated by the data augmentation algorithm.

Diabetic retinopathy is one of the main causes of vision loss, and it can be identified through ophthalmological examinations that aim to locate the presence of retinal lesions such as Microaneurysms, Hemorrhages, Soft Exudates, and Hard Exudates. The development of computerized approaches to perform the instance segmentation of these lesions can help in the early diagnosis of the disease. However, the segmentation of instances of artifacts in the retina is a complex task due to factors such as object size and morphological characteristics. This article proposes a new approach based on a Mask Regions with Convolutional Neural Network features (Mask R-CNN) architecture to perform instance segmentation of lesions associated with diabetic retinopathy. The proposed approach was trained, adjusted, and tested using different public datasets of diabetic retinopathy, which were implemented with the Detectron2 libraries and OpenCV. The best result obtained by the proposed approach in the Dataset for Diabetic Retinopathy (DDR) was using Tilling and Adam optimizer, reaching a mean Average Precision ( mAP ) of 0.2903 in the detection of fundus lesions for the limit of Intersection Over Union ( IoU ) of 0.5 in the validation stage and an mAP of 0.1670 in the detection of fundus lesions to the limit of IoU of 0.5 in the test step. The results obtained in the experiments demonstrate that the proposed approach presented promising results in the instance segmentation of microaneurysms with an increase in precision, which in our case reaches approximately 16%.

In this paper, we use data from the Microsoft Kinect sensor that processes the captured imageof a person using and extracting the joints information on every frame. Then, we propose the creation ofan image derived from all the sequential frames of a gesture the movement, which facilitates training in aconvolutional neural network. We trained a CNN using two strategies: combined training and individualtraining. The strategies were experimented in the convolutional neural network (CNN) using theMSRC-12 dataset, obtaining an accuracy rate of 86.67% in combined training and 90.78% of accuracyrate in the individual training.. Then, the trained neural network was used to classify data obtained fromKinect with a person, obtaining an accuracy rate of 72.08% in combined training and 81.25% inindividualized training. Finally, we use the system to send commands to a mobile robot in order to controlit.

Healthcare 5.0 represents the next evolution in intelligent and interconnected healthcare systems, leveraging emerging technologies such as Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) to enhance patient care and automation. While Intrusion Detection Systems (IDSs) are a critical component for securing these environments, the current literature lacks a systematic analysis that jointly evaluates the effectiveness of AI models, the suitability of datasets, and the role of Explainable Artificial Intelligence (XAI) in the Healthcare 5.0 landscape. To fill this gap, this survey provides a comprehensive review of IDSs for Healthcare 5.0, analyzing state-of-the-art approaches and available datasets. Furthermore, a practical case study is presented, demonstrating that the fusion of network and biomedical features significantly improves threat detection, with physiological signals proving crucial for identifying complex attacks like spoofing. The primary contribution is therefore an integrated analysis that bridges the gap between cybersecurity theory and clinical practice, offering a guide for researchers and practitioners aiming to develop more secure, transparent, and patient-centric systems.

Diabetes affects millions of people worldwide, and diabetic retinopathy is a complication of diabetes. Brazil is the sixth in the world in the incidence of diabetes and has the highest numbers in Latin America with 15.7 million adults affected by this condition. Typically diabetic retinopathy is identified by lesions such as hard and soft exudates, microaneurysms, and vitreous hemorrhages. Early diagnosis of these lesions is essential to prevent the progression of the disease to severe stages and the consequent loss of vision. As the disease diagnosis is based on image analysis, it is possible to use deep learning models to detect artifacts in the retina. This article proposes a new method that uses a YOLOR-CSP architecture combined with the Slicing Aided Hyper Inference framework to detect fundus lesions. The proposed method was trained, adjusted, and evaluated using the DDR dataset, obtaining a mAP equal to 38.08%. The proposed method achieved values of AP equal to 40.90%, 46.60%, 26.10%, and 38.70% for hard exudates, soft exudates, microaneurysms, and vitreous hemorrhages, respectively, surpassing similar studies found in the literature.