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.

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%.

The recognition images of cattle brand in an automatic way is a necessity to governmental organs responsible for this activity. To help this process, this work presents a method that consists in using Convolutional Neural Network for extracting of characteristics from images of cattle brand and Support Vector Machines for classification. This method consists of six stages: (a) select database of images; (b) select pre-trained CNN; (c) pre-process the images and apply CNN; (d) extract images of features; (e) train and sort images (SVM); (f) evaluate the classification results. The accuracy of the method was tested on database of municipal city hall, where it achieved satisfactory results, comparable to other methods from the literature, reporting 93.28% of accuracy and 12.716 seconds of processing time, respectively.

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.

Diabetic Retinopathy is one of the leading causes of vision loss and presents in its initial phase retinal lesions, such as microaneurysms, hemorrhages, and hard and soft exudates. Therefore, 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 define the best form of treatment. This work proposes a method based on deep neural network models that perform one-stage object detection, using state-of-the-art data augmentation and transfer learning techniques to present a model that aids in the medical diagnosis of fundus lesions. The model was trained, adjusted, and evaluated using the DDR Diabetic Retinopathy Dataset, and implemented based on the YOLOv5 architecture and the PyTorch framework, achieving values for mAP of 0.1040 and 0.0283 for IoU threshold of 0.5 and 0.5:0.95 respectively, in the validation set. The results obtained in the experiments demonstrate that the proposed method presented superior results to equivalent works found in the literature.