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Recent Publications

Pedro Castro, Gladston Moreira, and Eduardo Luz. 10/20/2021. “An End-to-End Deep Learning System for Hop Classification.” IEEE LATIN AMERICA TRANSACTIONS, 100, No. XXX (2021): Early Access. Publisher's VersionAbstract

Automatic classification of plant species is a very challenging and widely studied problem in the literature. Distinguishing different varieties within the same species is an even more challenging task although less explored. Nevertheless, for some species distinguishing the varieties within the species can be of paramount importance.
Hops, a plant widely used in beer production, has over 250 cataloged varieties. Although the varieties have similar appearances, their chemical components, which influence the aroma and flavor of the drink, are quite heterogeneous. Therefore, it is important for producers to distinguish which variety the plant belongs to in a simple manner.
In this work, an end-to-end deep learning system is proposed to automate the task of hop classification. Five architectures are proposed and evaluated with an uncontrolled environment dataset that includes 12 varieties of hops on 1592 images, from three different cell phone cameras. The best architecture automatically detects the hop leaves on the image and performs the classification using the information of up to 10 leaves. The method achieved an accuracy of 95.69% with an inference time of 672ms. To reach such figures, state-of-the-art convolutional blocks were explored along with data augmentation techniques. Our results show that the system is robust and has a low computational cost.

Pedro Castro, Eduardo Luz, and Gladston Moreira. 8/24/2021. “Dataset for Hop varieties classification.” Data in Brief, 38, Pp. 107312. Publisher's VersionAbstract
ABSTRACT Humulus lupulus L., also known as hops, is a vine whose flowers are a major component in brewing. It delivers flavor, bitterness, and aroma to beer and also aids in foam stabilization. Furthermore, it plays an important role in beer conservation due to its antimicrobial and antioxidant properties, which have recently been studied for food preservation. Hops can also be found in the production of cosmetics and is considered healthy food. There are more than 250 cataloged varieties of hops, and among the main attributes that differ from each other are alpha-acids, beta-acids, and essential oils. Those components give the beer a unique combination of characteristics, and may even influence its category. There are many ways to identify the hop variety from its acids and essential oils using methods such as chromatography, mass spectrometry, capillary electrophoresis, and nuclear magnetic resonance. However, these methods demand expensive and complex equipment, inaccessible or unavailable to most beer producers. In this work, we present a database that includes 1592 images of hop leaves, from 12 popular hop varieties in southeastern Brazil. From these images, it is possible to explore methods of pattern recognition and machine learning to classify hop varieties
Débora N. Diniz, Mariana T. Rezende, Andrea G. C. Bianchi, Claudia M. Carneiro, Eduardo J. S. Luz, Gladston J. P. Moreira, Daniela M. Ushizima, Fátima N. S. de Medeiros, and Marcone J. F. Souza. 7/9/2021. “A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification.” Journal of Imaging, 7, 7. Publisher's VersionAbstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
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