Deep manifold feature fusion for classification of breast histopathology images

2022 ◽  
pp. 103400
Author(s):  
Pin Wang ◽  
Pufei Li ◽  
Yongming Li ◽  
Jin Xu ◽  
Fang Yan ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Histopathology Images

Fine-grained Classification of Malicious Code Based on CNN and Multi-resolution Feature Fusion

2021 ◽  
Author(s):  
Junmiao Liang ◽  
Zhenhu Ning ◽  
Yihua Zhou ◽  
Dongzhi Cao
Keyword(s):  
Feature Fusion ◽  
Malicious Code ◽  
Fine Grained

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

2018 ◽  
Vol 58 (7) ◽  
pp. 1293-1299 ◽  
Author(s):  
Hongbing Wang ◽  
Rong Huang ◽  
Liyuan Gao ◽  
Weishen Wang ◽  
Anjun Xu ◽  
...  
Keyword(s):  
Wear Debris ◽  
Feature Fusion ◽  
Steel Production ◽  
Production Equipment ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

2020 ◽  
Vol 16 (3) ◽  
pp. 280-294 ◽  
Author(s):  
Toto Haryanto ◽  
Adib Pratama ◽  
Heru Suhartanto ◽  
Aniati Murni ◽  
Kusmardi Kusmardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Texture Feature ◽  
Gpu Acceleration ◽  
Texture Feature Extraction ◽  
Histopathology Images

Joint Segmentation and Fine-Grained Classification of Nuclei in Histopathology Images

2019 ◽  
Author(s):  
Hui Qu ◽  
Gregory Riedlinger ◽  
Pengxiang Wu ◽  
Qiaoying Huang ◽  
Jingru Yi ◽  
...  
Keyword(s):  
Fine Grained ◽  
Histopathology Images

Multi-label Classification of Hyperspectral Images Based on Label-Specific Feature Fusion

2021 ◽  
pp. 224-234
Author(s):  
Jing Zhang ◽  
PeiXian Ding ◽  
Shuai Fang
Keyword(s):  
Feature Fusion ◽  
Hyperspectral Images

scholarly journals Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks

2018 ◽  
Vol 84 ◽  
pp. 345-356 ◽  
Author(s):  
Baris Gecer ◽  
Selim Aksoy ◽  
Ezgi Mercan ◽  
Linda G. Shapiro ◽  
Donald L. Weaver ◽  
...  
Keyword(s):  
Convolutional Networks ◽  
Histopathology Images
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