Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.

Liver cancer is one of the most common types of cancer according to World Health Statistics. Computer-aided diagnosis (CAD) systems are used in medical imaging for liver tumor identification and classification. Texture is a type of feature that can provide measurements of properties such as smoothness and regularity of the image. Handcraft techniques based on fractal geometry allow quantifying self-similarity properties present in images. However, new studies have shown that using information obtained from deep-learned feature maps can maximize the results of classical classifiers. This work presents an approach that investigates descriptors obtained by handcrafted and deep learning features, feature selection methods and the Hermite polynomial (HP) algorithm to classifier liver histological images. The results were evaluated using metrics such as accuracy (ACC) and the imbalance accuracy metric (IAM). The association with fractal features, Lasso regularization and the HP algorithm achieved 0.98 of IAM and 99.53% ACC, which was relevant when evaluated with other studies in the literature.

The analysis of histological image features for automatic detection of pathologies plays an important role in medicine. Considering that, we proposed a method based on the association of features extracted by multi-scale and multidimensional fractal techniques, Haralick descriptors, and CNN for pattern recognition of colorectal cancer, breast cancer, and non-Hodgkin lymphomas. For feature selection, we applied the ReliefF algorithm to rank the best 50 features and then applied the evolutionary algorithms GWO, PSO, and GA. The classification was made with SVM, K*, and Random Forest algorithms. This strategy allows classifying plenty of feature vectors selected by different algorithms, and consequently, improves the accuracy of the interpretations about the class distinction of histological images. The best combination found was composed of GA and K* algorithms, resulting in 91.06%, 90.52% e 82.01% accuracy for colorectal cancer, breast cancer, and non-Hodgkin lymphomas respectively. The performance obtained by the method indicates that the feature association extracted by different approaches and their subsequent selection and classification presents a potential field for further studies with a high degree of contribution to science.

Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques.