Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Roberto, Guilherme Freire; Lumini, Alessandra; Neves, Leandro Alves; Nascimento, Marcelo Zanchetta do

doi:10.1016/j.eswa.2020.114103

Cited by 29 publications

(28 citation statements)

References 65 publications

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“…The first category consists of handcrafted features (HFs) defined by distinct extraction methods, usually aiming to overcome specific problems [18][19][20][21][22]. Among the HFs, it is possible to highlight techniques based on fractal geometry that use multiscale and multidimensional methods (Higuchi fractal dimension, probabilistic fractal dimension, box fusion fractal dimension, lacunarity and percolation) [23][24][25][26][27][28][29], Haralick [30] and local binary patterns (LBPs) [31]. For instance, Haralick and LBPs have been applied in several imaging contexts [32][33][34], exploring the identification of lung cancer subtypes [35], the presence of cancerous characteristics in breast tissue samples [18,36] and the classification of colorectal cancer [6].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Tenguam,

Longo,

Roberto

et al. 2024

Applied Sciences

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In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

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Section: Introductionmentioning

confidence: 99%

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Tenguam,

Longo,

Roberto

et al. 2024

Applied Sciences

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“…Colon cancer is not age-related, but it is more common in older people. 1,7,8 On the inner surface of the colon, it usually starts as smaller, noncancerous (benign) clusters of cells known as polyps. A few of these polyps can grow into colon cancer over time.…”

Section: Introductionmentioning

confidence: 99%

“…When the colon is infected with malignant cells, it can lead to colon cancer. Colon cancer is not age‐related, but it is more common in older people 1,7,8 . On the inner surface of the colon, it usually starts as smaller, noncancerous (benign) clusters of cells known as polyps.…”

Section: Introductionmentioning

confidence: 99%

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Raju

Rao

2022

Concurrency and Computation

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Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

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“…Moreover, it has been widely used in the medical field to perform classification [17], segmentation [12], and detection [18] tasks. Additionally, this approach has been applied to both images and signals such as MR images [17], histology images [19], iris images [18], electromyographic (EMG) signals [14], and electroencephalogram (EEG) signals [20]. The addition of multiresolution features such as Contourlet and Shearlet coefficients as handcrafted features with CNN's features is able to enrich the set of features with multiscale and multidirectional properties to perform the segmentation task.…”

Section: Introductionmentioning

confidence: 99%

Deep learning with multiresolution handcrafted features for brain MRI segmentation

Mecheter

Abbod

Amira

et al. 2022

Artificial Intelligence in Medicine

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Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Cited by 29 publications

References 65 publications

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Deep learning with multiresolution handcrafted features for brain MRI segmentation

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