scholarly journals Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014 ◽  
Vol 6 (8) ◽  
pp. 6867-6896 ◽  
Author(s):  
Arsalan Othman ◽  
Richard Gloaguen
Keyword(s):  
Morphological Features ◽  
Ophiolite Complex ◽  
Svm Classification ◽  
Lithological Mapping

Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features

2021 ◽  
pp. 177-191
Author(s):  
Natalia V. Revollo ◽  
G. Noelia Revollo Sarmiento ◽  
Claudio Delrieux ◽  
Marcela Herrera ◽  
Rolando González-José
Keyword(s):  
Machine Learning ◽  
Human Sperm ◽  
Sperm Head ◽  
Morphological Features ◽  
Supervised Machine Learning ◽  
Machine Learning Classification

scholarly journals Supervised SVM Classification of Rainfall Datasets

2017 ◽  
Vol 10 (15) ◽  
pp. 1-6
Author(s):  
K. Hari Prasada Raju ◽  
N. Sandhya ◽  
Raghav Mehra ◽  
◽  
◽  
...  
Keyword(s):  
Svm Classification

scholarly journals A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mengwan Wei ◽  
Yongzhao Du ◽  
Xiuming Wu ◽  
Qichen Su ◽  
Jianqing Zhu ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Morphological Features ◽  
High Dimensional ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

scholarly journals Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

2019 ◽  
Vol 9 (15) ◽  
pp. 2969 ◽  
Author(s):  
Bhattacharjee ◽  
Park ◽  
Kim ◽  
Prakash ◽  
Madusanka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Features ◽  
Morphological Features ◽  
Support Vector ◽  
Epithelial Tissue ◽  
Gleason Grade ◽  
Svm Classification ◽  
Gleason Grading ◽  
Grade 5 ◽  
Grade 3

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature.

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