Covid-19 is a serious disease caused by the Sars-CoV-2 virus that has been first reported in China at late 2019 and has rapidly spread around the world. As the virus affects mostly the lungs, chest X-rays are one of the safest and most accessible ways of diagnosing the infection. In this paper, we propose the use of an approach for detecting Covid-19 in chest X-ray images through the extraction and classification of local and global percolation-based features. The method was applied in two datasets: one containing 2,002 segmented samples split into two classes (Covid-19 and Healthy); and another containing 1,125 non-segmented samples split into three classes (Covid-19, Healthy and Pneumonia). The 48 obtained percolation features were given as input to six different classifiers and then AUC and accuracy values were evaluated. We employed the 10-fold cross-validation method and evaluated the lesion sub-types with binary and multiclass classification using the Hermite Polynomial classifier, which had never been employed in this context. This classifier provided the best overall results when compared to other five machine learning algorithms. These results based in the association of percolation features and Hermite polynomial can contribute to the detection of the lesions by supporting specialists in clinical practices.

Deep learning methods have achieved impressive results for object detection, but they usually require powerful GPUs and large annotated datasets. In contrast, there is a lack of explainable networks in the literature. For instance, Feature Learning from Image Markers (FLIM) is a feature extraction strategy for lightweight CNNs without backpropagation that requires only a few training images. In this work, we extend FLIM for general image graph modeling, allowing it for a non-strict kernel shape and taking advantage of the adjacency relation between nodes to extract feature vectors based on neighbors’ features. To produce saliency maps by combining learned features, we proposed a User-Guided Decoder (UGD) that does not require training and is suitable for any FLIM-based strategy. Our results indicate that the proposed Graph-based FLIM, named GFLIM, not only outperforms FLIM but also produces competitive detections with deep models, even having an architecture thousands of times smaller in number of parameters.

Many different cultures and countries have fish as a central piece in their diet, particularly in coastal countries such as Portugal, with the fishery and aquaculture sectors playing an increasingly important role in the provision of food and nutrition. As a consequence, fish-freshness evaluation is very important, although so far it has relied on human judgement, which may not be the most reliable at times.

This paper proposes an automated non-invasive system for fish-freshness classification, which takes fish images as input. The paper also proposes a new of seabream image dataset, which will be made publicly available for academic and scientific purposes with the publication of this paper. The dataset includes metadata, such as manually generated segmentation masks corresponding to the fish eye and body regions, as well as the time since capture.

For fish-freshness classification four freshness levels are considered: very-fresh, fresh, not-fresh and spoiled. The proposed system starts with an image segmentation stage, with the goal of automatically segmenting the fish eye region, followed by freshness classification based on the eye characteristics. The system employs transformers, for the first time in fish-freshness classification, both in the segmentation process with the Segformer and in feature extraction and freshness classification, using the Vision Transformer (ViT).

Encouraging results have been obtained, with the automatic fish eye region segmentation reaching a detection rate of 98.77\%, an accuracy of 96.28\% and a value of the Intersection over Union (IoU) metric of 85.7\%. The adopted ViT classification model, using a 5-fold cross-validation strategy, achieved a final classification accuracy of 80.8\% and an F1 score of 81.0\%, despite the relatively small dataset available for training purposes.