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Lumini A, Roberto GF, Neves LA, Martins AS, do Nascimento MZ. Percolation Images: Fractal Geometry Features for Brain Tumor Classification. ADVANCES IN NEUROBIOLOGY 2024; 36:557-570. [PMID: 38468053 DOI: 10.1007/978-3-031-47606-8_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Brain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a "percolation" image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.
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Affiliation(s)
- Alessandra Lumini
- Department of Computer Science and Engineering, University of Bologna, Cesena, FC, Italy.
| | - Guilherme Freire Roberto
- Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, SP, Brazil
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto, SP, Brazil
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Longo LHDC, Roberto GF, Tosta TAA, de Faria PR, Loyola AM, Cardoso SV, Silva AB, do Nascimento MZ, Neves LA. Classification of Multiple H&E Images via an Ensemble Computational Scheme. ENTROPY (BASEL, SWITZERLAND) 2023; 26:34. [PMID: 38248160 PMCID: PMC10814107 DOI: 10.3390/e26010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024]
Abstract
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. 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.
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Affiliation(s)
- Leonardo H. da Costa Longo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
| | - Guilherme F. Roberto
- Department of Informatics Engineering, Faculty of Engineering, University of Porto, Dr. Roberto Frias, sn, 4200-465 Porto, Portugal;
| | - Thaína A. A. Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, São José dos Campos 12247-014, SP, Brazil;
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, Uberlândia 38405-320, MG, Brazil;
| | - Adriano M. Loyola
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Sérgio V. Cardoso
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Adriano B. Silva
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Marcelo Z. do Nascimento
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
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Azevedo Tosta TA, de Faria PR, Neves LA, do Nascimento MZ. Evaluation of statistical and Haralick texture features for lymphoma histological images classification. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2021.1902401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Thaína A. Azevedo Tosta
- Center of Mathematics, Computer Science and Cognition, Federal University of ABC (UFABC), Santo André, Brazil
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), São José dos Campos, Brazil
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Uberlândia, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics, São Paulo State University (UNESP), São José do Rio Preto, Brazil
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Ferjaoui R, Cherni MA, Boujnah S, Kraiem NEH, Kraiem T. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106320. [PMID: 34390938 DOI: 10.1016/j.cmpb.2021.106320] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. METHODS The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. RESULTS The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F1-measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%. CONCLUSIONS Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.
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Affiliation(s)
- Radhia Ferjaoui
- University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia.
| | - Mohamed Ali Cherni
- University of Tunis, LR13 ES03 SIME Laboratory, ENSIT, Montfleury 1008 Tunisia
| | - Sana Boujnah
- University of Tunis El Manar, National Engineering School of Tunis, Tunisia
| | | | - Tarek Kraiem
- University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, 1007, Tunisia; University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia
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Martins AS, Neves LA, de Faria PR, Tosta TAA, Longo LC, Silva AB, Roberto GF, do Nascimento MZ. A Hermite polynomial algorithm for detection of lesions in lymphoma images. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00927-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Analysis of cancer in histological images: employing an approach based on genetic algorithm. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00931-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Dif N, Elberrichi Z. Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2020. [DOI: 10.4018/ijcini.2020100104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.
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Affiliation(s)
- Nassima Dif
- EEDIS Laboratory ,Djillali Liabes University, Sidi Bel Abbes, Algeria
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Azevedo Tosta TA, de Faria PR, Neves LA, do Nascimento MZ. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artif Intell Med 2019; 95:118-132. [DOI: 10.1016/j.artmed.2018.10.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/13/2018] [Accepted: 10/20/2018] [Indexed: 01/13/2023]
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de Meneses FGA, Lima GD, Nunes M, Hugo Bastos V, Teixeira S. Percolation theory for the recognition of patterns in topographic images of the cortical activity. Med Hypotheses 2019; 125:37-40. [PMID: 30902149 DOI: 10.1016/j.mehy.2019.02.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 01/23/2019] [Accepted: 02/03/2019] [Indexed: 11/24/2022]
Abstract
Electroencephalogram (EEG) is one of the mechanisms used to collect complex data. Its use includes evaluating neurological disorders, investigating brain function and correlations between EEG signals and real or imagined movements. The Topographic Image of Cortical Activity (TICA) records obtained by the EEG make it possible to observe, through color discrimination, the cortical areas that represent greater or lesser activity. Percolation Theory (PT) reveals properties on the aspects of fluid spreading from a central point, these properties being related to the aspects of the medium, topological characteristics and ease of penetration of a fluid in materials. The hypothesis presented so far considers that synaptic activities originate in points and spread from them, causing different areas of the brain to interact in a diffusive associative behavior, generating electric and magnetic fields by the currents that spread through the brain tissue and have an effect on the scalp sensors. Brain areas spatially separated create large-scale dynamic networks that are described by functional and effective connectivity. The proposition is that this phenomenon behaves like a fluidic spreading, so we can use the PT, through the topological analysis we detect specific signatures related to neural phenomena that manifest changes in the behavior of synaptic diffusion. This signature must be characterized by the Fractal Dimension (FD) values of the scattering clusters, these values will be used as properties in the k-Nearest Neighbors (kNN) method, an TICA will be categorized according to the degree of similarity to the preexisting patterns. In this context, our hypothesis will consolidate as a more computational resource in the service of medicine and another way that opens with the possibility of analysis and detailed inferences of the brain through TICA that go beyond a simply visual observation, as it happens in the present day.
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Affiliation(s)
- Francisco Gerson A de Meneses
- Neuroinnovation Technology & Brain Mapping Laboratory, Federal University of Piauí, Parnaíba, Brazil; The Northeast Biotechnology Network, Federal University of Piauí, Teresina, Brazil.
| | - Gildário Dias Lima
- Neuroinnovation Technology & Brain Mapping Laboratory, Federal University of Piauí, Parnaíba, Brazil
| | - Monara Nunes
- Neuroinnovation Technology & Brain Mapping Laboratory, Federal University of Piauí, Parnaíba, Brazil
| | - Victor Hugo Bastos
- Neuroinnovation Technology & Brain Mapping Laboratory, Federal University of Piauí, Parnaíba, Brazil; Master Program in Biotechnology, Federal University of Piauí, Parnaíba, Brazil; The Northeast Biotechnology Network, Federal University of Piauí, Teresina, Brazil
| | - Silmar Teixeira
- Neuroinnovation Technology & Brain Mapping Laboratory, Federal University of Piauí, Parnaíba, Brazil; Master Program in Biotechnology, Federal University of Piauí, Parnaíba, Brazil; The Northeast Biotechnology Network, Federal University of Piauí, Teresina, Brazil
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Segato Dos Santos LF, Neves LA, Rozendo GB, Ribeiro MG, Zanchetta do Nascimento M, Azevedo Tosta TA. Multidimensional and fuzzy sample entropy (SampEn MF) for quantifying H&E histological images of colorectal cancer. Comput Biol Med 2018; 103:148-160. [PMID: 30368171 DOI: 10.1016/j.compbiomed.2018.10.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 09/22/2018] [Accepted: 10/13/2018] [Indexed: 12/23/2022]
Abstract
In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance. The fuzzy strategy was a Gaussian function used to verify the pertinence of the distances between windows. The result was a method capable of computing similarities between pixels contained in windows of various sizes, as well as the information present in the colour channels. The power of quantification was tested in a public colorectal image dataset, which was composed of both benign and malignant classes. The results were given as inputs for classifiers of different categories and analysed by applying the k-fold cross-validation and holdout methods. The derived performances indicate that the proposed association was capable of distinguishing the benign and malignant groups, with values that surpassed those results obtained with important techniques available in the Literature. The best performance was an AUC value of 0.983, an important result, mainly when we consider the difficulties of clinical practice for the diagnosis of the colorectal cancer.
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Affiliation(s)
- Luiz Fernando Segato Dos Santos
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Guilherme Botazzo Rozendo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Matheus Gonçalves Ribeiro
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Marcelo Zanchetta do Nascimento
- Faculty of Computation (FACOM), Federal University of Uberlândia (UFU), Avenida João Neves de Ávila 2121, Bl.B, 38400-902, Uberlândia, Minas Gerais, Brazil.
| | - Thaína Aparecida Azevedo Tosta
- Center of Mathematics, Computing and Cognition, Federal University of ABC (UFABC), Avenida dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil.
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do Nascimento MZ, Martins AS, Azevedo Tosta TA, Neves LA. Lymphoma images analysis using morphological and non-morphological descriptors for classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:65-77. [PMID: 30119858 DOI: 10.1016/j.cmpb.2018.05.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 06/08/2023]
Abstract
Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.
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Affiliation(s)
- Marcelo Zanchetta do Nascimento
- UFU - FACOM, av. João Neves de Ávila 2121, Bl.B, Uberlândia-MG 38400-902, Brazil; UFABC - CMCC, av. dos Estados 5001, Bl.B, St. André-SP 09210-580, Brazil.
| | | | | | - Leandro Alves Neves
- UNESP - DCCE, r. Cristóvão Colombo 2265, S.J. Rio Preto-SP 15054-000, Brazil
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