Hybrid Algorithm for the Classification of Fractal Designs and Images

Yu, Zhenhua; Sohail, Ayesha; Jamil, Maryam; Beg, O. A.; Tavares, João Manuel R. S.

doi:10.1142/s0218348x23400030

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…In addition, considering that the annotation of 3D dental models is a time-consuming task, the weakly supervised point cloud segmentation method is a promising research direction, and in the future we may be able to obtain good tooth segmentation results using incompletely annotated dental models. Finally, collaboration with other areas of work may be beneficial to the depth of this study [ 34 , 35 ], and such collaboration has an important role to play in the development of each area.…”

Section: Discussionmentioning

confidence: 99%

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Yang

Zhai

et al. 2022

Healthcare

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Tooth segmentation is an important aspect of virtual orthodontic systems. In some existing studies using deep learning-based tooth segmentation methods, the feature learning of point coordinate information and normal vector information is not effectively distinguished. This will lead to the feature information of these two methods not producing complementary intermingling. To address this problem, a tooth segmentation method based on multiple geometric feature learning is proposed in this paper. First, the spatial transformation (T-Net) module is used to complete the alignment of dental model mesh features. Second, a multiple geometric feature learning module is designed to encode and enhance the centroid coordinates and normal vectors of each triangular mesh to highlight the differences between geometric features of different meshes. Finally, for local to global fusion features, feature downscaling and channel optimization are accomplished layer by layer using multilayer perceptron (MLP) and efficient channel attention (ECA). The experimental results show that our algorithm achieves better accuracy and efficiency of tooth segmentation and can assist dentists in their treatment work.

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Section: Discussionmentioning

confidence: 99%

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Yang

Zhai

et al. 2022

Healthcare

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“…In this paper, we propose and implement a novel fast policy matching model, termed Poliseek, which can handle requests with intervals correctly and can be evaluated on various real-world [48][49][50] test policies. The design of Poliseek integrates three modules for fast policy match by combining linear dimensionality reduction and effectively minimizing interval collision.…”

Section: Discussionmentioning

confidence: 99%

Poliseek: A Fast XACML Policy Evaluation Engine Using Dimensionality Reduction and Characterized Search

Fan

Zhan

et al. 2022

Mathematics

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Due to evaluation performance limits and compatibility problems with PDP (Policy Decision Point) in practical information systems, some established schemes have limits in handling massive complex requests. To address the existing challenges of fast rule match on interval values, we propose a novel policy evaluation engine, namely Poliseek with three desired modules. A preprocessing module of Poliseek is equipped with a static encoding strategy and converts the XACML rules and requests into four-dimensional numeric vectors in an attribute space. Owing to a novel optimization object of minimizing interval collisions, a dimensionality reducer and diffuser module can generate candidate values related to each rule vector in the identification space. These values and requests are handled by a fast policy evaluation module using well-constructed hash buckets and a characterized search algorithm. The experimental results show that if the number of requests reaches 10,000, Poliseek can find the target rule approximately 1090, 15, and 15 times faster than the Sun PDP, XEngine, and SBA-XACML, respectively. Poliseek also offers a fast evaluation progress of handling 10,000 complex policy rules with interval attribute values in 275.9 ms, which shows its strong robustness and practicality.

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“…In addition, techniques that involve fractals at multiple scales and/or dimensions have also been applied to quantify the pathological architectures of tumors [23,25,26], demonstrating relevant results in the pattern recognition of prostate cancer [37], lymphomas [38], intraepithelial neoplasia [39], breast tumors [40], colorectal cancer [13] and psoriatic lesions [41]. Moreover, fractal methods are important for texture analysis because they provide information about the complexity of textures and patterns that are similar at various levels of magnification [42].…”

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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Hybrid Algorithm for the Classification of Fractal Designs and Images

Cited by 14 publications

References 19 publications

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Poliseek: A Fast XACML Policy Evaluation Engine Using Dimensionality Reduction and Characterized Search

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

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