This paper presents a methodology for classifying plant leaves on the basis of handcrafted features derived from the multiscale entropy of curvature and texture, as well as deep features obtained from convolutional neural networks (CNNs). We propose three object descriptors on the basis of the multiscale entropy of curvature. These object descriptors rely on the differential entropy of the probability distributions of multiscale curvatures to create a coarse-to-fine representation of the shape contour. Thus, we present a descriptor that aggregates the multiscale entropy of curvature, bending energy of curvature, and texture features to improve feature extraction of object signatures and subtle texture details of leaf images. The texture descriptor combines the statistics of the local binary pattern and gray-level co-occurrence matrix. We compare our handcrafted descriptors with deep features from various CNNs in multiclass classification using the random forest classifier, replacing the fully connected layer of the CNNs with this classifier. The experiments were conducted on four public leaf datasets: Plantscan, MED117, Flavia, and Swedish. The results of the F1-score and accuracy metrics, which exceed 99.50%, validate the aggregation strategy and show that it is competitive and powerful. The results also confirm that the proposed strategy outperformed six different sets of deep features according to the F1-score and accuracy. Moreover, the handcrafted descriptors achieved better results with 40 features than LeNet’s 50 features. The qualitative analysis of the multidimensional data visualization results prove that combining different shape features and texture details improved the description of the leaf images, as it provided better intraclass compactness and interclass separation in these datasets.

Although implementing the Pap smear has drastically reduced the mortality rates from cervical cancer, false positives and negatives are related to the quality of the analysis and the cytopathologist experience. An alternative is the insertion of digital cytology in the quality monitoring to assist the screening. However, conventional cytology is still a major challenge, as it presents a lot of cellular overlap and several epithelial structures that make it difficult to implement computational methodologies. This article compares the performance of U-net and SegNet neural networks for nuclei segmentation in cervical images. Experiments were performed with different activation functions, batch sizes, and datasets, ISBI (synthetic images from liquid cytology) and CRIC Cervix-Seg (conventional cytology real images). The models achieved a Dice coefficient of 0.9783 for ISBI2015 and 0.9429 for CRIC Cervix-Seg. These results suggest a methodology capable of segmenting real images of cervical nuclei with quality, even in situations of overlap and artefacts, advancing efforts towards the automation of tasks as part of the cytopathological analysis in the laboratory work routine.

This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval.

Este artigo apresenta uma nova metodologia baseada em aprendizado profundo para detectar lesões cervicais em amostras de exame de Papanicolau. O modelo proposto utiliza informações de localização do núcleo e realiza recortes em torno dele usando diferentes dimensões, sem a necessidade de segmentação da imagem. Vários modelos de CNN foram desenvolvidos e treinados com imagens reais de células cervicais. Os resultados mostraram que o modelo atingiu uma acurácia satisfatória de 0,94 usando o tamanho de caixa de 70x70 sem a necessidade de segmentar imagens. Acredita-se que essa metodologia possa auxiliar os citopatologistas na melhoria do diagnóstico e na qualidade dos resultados dos laboratórios, contribuindo para a prevenção do câncer de colo do útero.

There are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving 285 million visually impaired people. Information and communication technologies can help disabled people achieve greater independence, quality of life, and inclusion in social activities by increasing, maintaining, or improving their functional capacities. This paper presents a significant evaluation of local and deep features for an automatic methodology for identifying banknotes. To determine the best local features, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies. To define the deep features, we extract features using three pre-trained well-known CNNs. Additionally, we evaluated using a hybrid approach formed by combining local and deep features. In this situation, the features were selected according to their gain ratios and used as input to the classifier. Experiments performed on US dollar (USD), euro (EUR), and Brazilian real banknotes (BRL) obtained accuracy rates of 99.96%, 99.12%, and 96.92%, respectively.

An accurate early-stage leukemia diagnosis plays a critical role in treating and saving patients' lives. The two primary forms of leukemia are acute and chronic leukemia, which is subdivided into myeloid and lymphoid leukemia. Deep learning models have been increasingly used in computer-aided medical diagnosis (CAD) systems developed to detect leukemia. This article assesses the impact of widely applied techniques, mainly data aug-mentation and multilevel and ensemble configurations, in deep learning-based CAD sys-tems. Our assessment included five scenarios: three binary classification problems and two multiclass classification problems. The evaluation was performed using 3,536 images from 18 datasets, and it was possible to conclude that data augmentation techniques improve the performance of convolutional neural networks (CNNs). Furthermore, there is an improvement in the classification results using a combination of CNNs. For the binary problems, the performance of the ensemble configuration was superior to that of the mul-tilevel configuration. However, the results were statistically similar in multiclass scenarios. The results were promising, with accuracies of 94.73% and 94.59% obtained using multi-level and ensemble configurations in a scenario with four classes. The combination of methods helps to reduce the error or variance of the predictions, which improves the accu-racy of the used deep learning-based model.(c) 2022 Published by Elsevier Inc.

A análise de células cervicais a partir de exames de Papanicolaou convencionais ainda é um grande desafio. Diferentemente das imagens de exame em meio líquido, a citologia convencional possui muita sobreposição celular e diversas estruturas epiteliais que dificultam a implementação de metodologias computacionais que possam dar suporte à automação dos exames. Este trabalho compara o desempenho de duas arquiteturas de redes neurais convolucionais para segmentar núcleos celulares. A avaliação considerou o uso de um novo banco de dados de segmentação. Os resultados mostraram que nossa proposta consegue segmentar núcleos celulares a partir de imagens de citologia convencional com múltiplas células e sobreposição.

Screening of Pap smear images continues to depend upon cytopathologists’ manual scrutiny, and the results are highly influenced by professional experience, leading to varying degrees of cell classification inaccuracies. In order to improve the quality of the Pap smear results, several efforts have been made to create software to automate and standardize the processing of medical images. In this work, we developed the CEA (Cytopathologist Eye Assistant), an easy-to-use tool to aid cytopathologists in performing their daily activities. In addition, the tool was tested by a group of cytopathologists, whose feedback indicates that CEA could be a valuable tool to be integrated into Pap smear image analysis routines. For the construction of the tool, we evaluate different YOLO configurations and classification approaches. The best combination of algorithms uses YOLOv5s as a detection algorithm and an ensemble of EfficientNets as a classification algorithm. This configuration achieved 0.726 precision, 0.906 recall, and 0.805 F1-score when considering individual cells. We also made an analysis to classify the image as a whole, in which case, the best configuration was the YOLOv5s to perform the detection and classification tasks, and it achieved 0.975 precision, 0.992 recall, 0.970 accuracy, and 0.983 F1-score.

Despite research on cervical cells since 1925, systems to automatically screen images from conventional Pap smear tests continue to be unavailable. One of the main challenges in deploying precise software tools is to validate cell signatures. In this paper, we introduce an analysis framework, CRIC-feat, that expedites the investigation of different image databases and respective descriptors, particularly applicable to Pap images. This paper provides a three-fold contribution: 1) we first review and discuss the main feature extraction protocols for cell description and implementations suitable for cervical cells; 2) we present a new application of Gray level run length (GLRLM) features to Pap images; 3) we evaluate 93 cell classification approaches, and provide a guideline for obtaining the most accurate description, based on two current public databases with digital images of real cells. Finally, we show that the nucleus information is preponderant in cell classification, particularly when considering the GLRLM feature set.

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988).

The nuclei and cytoplasm segmentation of cervical cells is a well studied problem. However, the current segmentation algorithms are not robust to clinical practice due to the high computational cost or because they cannot accurately segment cells with high overlapping. In this paper, we propose a method that is capable of segmenting both cytoplasm and nucleus of each individual cell in a clump of overlapping cells. The proposed method consists of three steps: 1) cellular mass segmentation; 2) nucleus segmentation; 3) cytoplasm identification based on an active contour method. We carried out experiments on both synthetic and real cell images. The performance evaluation of the proposed method showed that it was less sensitive to the increase in the number of cells per image and the overlapping ratio against two other existing algorithms. It has also achieved a promising low processing time and, hence, it has the potential to support expert systems for cervical cell recognition.

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.

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.

Around the world, there are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving a total of 285 million visually impaired people. The use of information and communication technologies can help disabled people to achieve greater independence, quality of life and inclusion in social activities by increasing, maintaining or improving their functional capacities. In this context, this paper presents an automatic methodology for identifying banknotes that can be widely used by people with visual impairment. For this, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies, which can be used as a basis for the development of applications for the identification of banknotes. Experiments performed on US Dollar (USD), Euro (EUR) and Brazilian Real Banknotes (BRL) obtained rates of accuracy of 99.78%, 99.12%, and 96.92%, respectively.

One of the main challenges for cell segmentation is to separate overlapping cells, which is also a challenging task for cytologists. Here we propose a method that combines different algorithms for cervical cell segmentation of Pap smear images and searches for the best result underlying the maximization of a similarity coefficient. We carried out experiments with three state-of-the-art segmentation algorithms on images with clumps of cervical cells. We extracted features such as coefficient of variation and overlapping ratios for each cell grouping and selected the most appropriate algorithm to segment each cell clump. For decision criterion, we identified the cell clumps of the training dataset and calculated the mentioned features. We segmented each clump by the algorithms and reckoned the Dice measure from each segmentation. Finally, we used the kNN classifier to predict the best algorithm among neighboring k-clumps by choosing the one with the largest number of wins. We validated our proposal on multifocal cervical cell images and obtained an average Dice around 76.6% without using a threshold value. These results demonstrated that the proposed ensemble of segmentation algorithms is promising and suitable for cervical cell image segmentation.

The focus of this work is on the detection of nuclei in synthetic images of cervical cells. Finding nuclei is an important step in building a computational method to help cytopathologists identify cell changes from Pap smears. The method developed in this work combines both the Multi-Start and the Iterated Local Search metaheuristics and uses the features of a region to identify a nucleus. It aims to improve the assertiveness of the screening and reduce the professional workload. The irace package was used to automatically calibrate all parameter values of the method. The proposed approach was compared with other methods in the literature according to recall, precision, and F1 metrics using the ISBI Overlapping Cytology Image Segmentation Challenge database (2014). The results show that the proposed method has the second-best values of F1 and recall, while the accuracy is still high.

This paper investigates the feasibility of combining sustained vowels for computer-based pathological voice characterization. Although recent work has shown that single sustained vowels enable accurate voice characterization, the literature lacks evidence about combining them. Here we propose a pipeline for abnormal voice characterization and classification that encompasses a feature extraction method based on the Haar wavelet. We also evaluate the benefits of our pipeline when using an input signal that comprises three different sustained vowels instead of single ones. We conducted experiments on samples of sustained vowels /a/, /i/, and /u/ from two public datasets, exploring the wavelet decomposition levels in the range of 4 to 18. Our results revealed that wavelet coefficients extracted from the combination of vowels improved signal description and, hence, identification of subtle features of pathological voices. We also showed that the Haar wavelet-based features extracted from combined vowels achieved accurate voice classification with fewer decomposition levels. This approach enabled accuracy improvements of at least 15.61 and 2.61% for SVD and AVFAD datasets, respectively, regardless of the biological gender.

This work presents a proposal for an efficient classification of cervical cells based on non-geometric characteristics extracted from nuclear regions of interested. This approach is based on the hypothesis that the nuclei store much of the information about the lesions in addition to their areas being more visible even with a high level of celular overlap, a common fact in the Pap smears images. Classification systems were used in two and three classes for a set of real images of the cervix from a supervised learning method. The results demonstrate high classification performance and high efficiency for applicability in realistic environments, both computational and biological.