Eye diseases have been a severe problem worldwide, especially in developing countries where technology and finance are limited. Today, the problem is being resolved thanks to pattern classification that is part of pattern recognition, and its primary goal is to group standard features from any entity, object, phenomenon, or event belonging to the real or abstract world. Convolutional Neural Networks are a type of Artificial Neural Network that is much used in intelligent pattern classification, learning machine, and data mining. Also, these algorithms are applied in medicine and ophthalmology for detecting diseases in the human body. This work presents a novel intelligent pattern classification algorithm based on a convolutional neural network, which is validated through the K-Fold Cross Validation test. Two different groups of retinography images are given: Glaucoma and Diabetic Retinopathy. The result of accuracy percentage was of 99.89%. Numerical metrics: Accuracy, Recall, Specificity Precision and F1score with values close to 1, and ROC curves support the suitable performance of the proposed classifier.

Background: Ocular diseases have been a severe problem worldwide, specifically in underdeveloped countries that do not have enough technology or economy to treat them. It would be beneficial to have software with low installation complexity and ease of use, allowing high efficacy in diagnosing eye diseases. This study aims to design and implement an algorithm based on deep learning to classify ocular diseases with high precision. Methods: This work describes digital image processing techniques for the easier handling of eye images; in particular, blur filters were used. The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. The validation algorithm used in this work was the hold-out algorithm (80–20). The metrics used to evaluate our proposal were the confusion matrix, accuracy, recall precision, and F1-score. Results: The dataset has five classes, namely, normal, cataract, diabetic retinopathy, glaucoma, and other retina diseases. The network architecture consists of 11 layers, including three convolutional layers, three max pooling layers, one batch normalization layer, one flattening layer, two hidden layers, and one output layer. This model resulted in 97% efficiency across all metrics. Conclusions: With the individual analysis of each metric, it can be observed that the proposed algorithm is capable of differentiating, first, images of healthy eyes from diseased ones and, second, adequately classifying eye diseases.

The following paper evaluates a watermark algorithm designed for digital images by using a perceptive mask and a normalization process, thus preventing human eye detection, as well as ensuring its robustness against common processing and geometric attacks. The Hermite transform is employed because it allows a perfect reconstruction of the image, while incorporating human visual system properties; moreover, it is based on the Gaussian functions derivates. The applied watermark represents information of the digital image proprietor. The extraction process is blind, because it does not require the original image. The following techniques were utilized in the evaluation of the algorithm: peak signal-to-noise ratio, the structural similarity index average, the normalized crossed correlation, and bit error rate. Several watermark extraction tests were performed, with against geometric and common processing attacks. It allowed us to identify how many bits in the watermark can be modified for its adequate extraction.

The face is the reflection of our emotions. We can guess the state of mind of a person by observing the face. In this paper, we applied an Associative Model algorithm to recognized Grammatical Facial Expressions. We used the dataset of the Brazilian sign language (Libras) system. The model we applied was a Morphological Associative Memory. We implemented a memory for each expression. The average of recognition for the same expression was of 98.89%. When we compare one expression with the others, we obtained a 98.59%, which means that our proposal confuses few expressions.