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The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.<\/jats:p>","DOI":"10.3390\/e26010034","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:35:21Z","timestamp":1703756121000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Classification of Multiple H&amp;E Images via an Ensemble Computational Scheme"],"prefix":"10.3390","volume":"26","author":[{"given":"Leonardo H. da Costa","family":"Longo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Statistics (DCCE), S\u00e3o Paulo State University (UNESP), Rua Crist\u00f3v\u00e3o Colombo, 2265, S\u00e3o Jos\u00e9 do Rio Preto 15054-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5883-2983","authenticated-orcid":false,"given":"Guilherme F.","family":"Roberto","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering, Faculty of Engineering, University of Porto, Dr. Roberto Frias, sn, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9291-8892","authenticated-orcid":false,"given":"Tha\u00edna A. A.","family":"Tosta","sequence":"additional","affiliation":[{"name":"Science and Technology Institute, Federal University of S\u00e3o Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, S\u00e3o Jos\u00e9 dos Campos 12247-014, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2650-3960","authenticated-orcid":false,"given":"Paulo R.","family":"de Faria","sequence":"additional","affiliation":[{"name":"Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberl\u00e2ndia (UFU), Av. Amazonas, S\/N, Uberl\u00e2ndia 38405-320, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9707-9365","authenticated-orcid":false,"given":"Adriano M.","family":"Loyola","sequence":"additional","affiliation":[{"name":"Area of Oral Pathology, School of Dentistry, Federal University of Uberl\u00e2ndia (UFU), R. 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