scholarly journals Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models

2021 ◽  
Vol 27 (4) ◽  
pp. 298-306
Author(s):  
Audrey K. C. Huong ◽  
Kim Gaik Tay ◽  
Xavier T. I. Ngu
Keyword(s):  
Image Classification ◽  
Pap Smear ◽  
False Negative ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Training Time ◽  
Binary Coding ◽  
Svm Model

Objectives: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.Methods: This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.Results: Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.Conclusions: We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

scholarly journals Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images

2020 ◽  
Author(s):  
Ning Yang ◽  
Faming Liu ◽  
Chunlong Li ◽  
Wenqing Xiao ◽  
Shuangcong Xie ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Svm Model ◽  
Multiple Comparison Correction

Abstract We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Finally, using the radiomics features as an input, a support vector machine (SVM) model was constructed to classify patients with COVID-19 and patients with other pneumonias. This model used 20 rounds of 10-fold cross-validation for training and testing. In the COVID-19 patients, correlation analysis (multiple comparison correction—Bonferroni correction, p<0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The results showed that the proposed method had a classification accuracy as high as 88.33%, sensitivity of 83.56%, specificity of 93.11%, and an area under the curve of 0.947. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some texture features were positively correlated with WBC, NE, and CRP and also negatively related to SPO2H and NE.

scholarly journals Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

2020 ◽  
Vol 12 (12) ◽  
pp. 1973
Author(s):  
Alim Samat ◽  
Erzhu Li ◽  
Wei Wang ◽  
Sicong Liu ◽  
Cong Lin ◽  
...  
Keyword(s):  
Random Forest ◽  
Image Classification ◽  
Classification Accuracy ◽  
Regression Tree ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

scholarly journals The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1784
Author(s):  
Shih-Chieh Chang ◽  
Chan-Lin Chu ◽  
Chih-Kuang Chen ◽  
Hsiang-Ning Chang ◽  
Alice M. K. Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Balance Test ◽  
Post Stroke ◽  
Feature Importance ◽  
Value Range ◽  
Importance Analysis ◽  
Partial Dependence

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.

scholarly journals Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application

2018 ◽  
Vol 8 (12) ◽  
pp. 2649 ◽  
Author(s):  
Balakrishnan Ramalingam ◽  
Anirudh Lakshmanan ◽  
Muhammad Ilyas ◽  
Anh Le ◽  
Mohan Elara
Keyword(s):  
Binary Classification ◽  
Support Vector ◽  
Single Shot ◽  
Machine Learning Technique ◽  
Cleaning Robot ◽  
Svm Model ◽  
Learning Technique ◽  
Essential Function ◽  
Cleaning Robots

Debris detection and classification is an essential function for autonomous floor-cleaning robots. It enables floor-cleaning robots to identify and avoid hard-to-clean debris, specifically large liquid spillage debris. This paper proposes a debris-detection and classification scheme for an autonomous floor-cleaning robot using a deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) cascaded technique. The SSD (Single-Shot MultiBox Detector) MobileNet CNN architecture is used for classifying the solid and liquid spill debris on the floor through the captured image. Then, the SVM model is employed for binary classification of liquid spillage regions based on size, which helps floor-cleaning devices to identify the larger liquid spillage debris regions, considered as hard-to-clean debris in this work. The experimental results prove that the proposed technique can efficiently detect and classify the debris on the floor and achieves 95.5% percent classification accuracy. The cascaded approach takes approximately 71 milliseconds for the entire process of debris detection and classification, which implies that the proposed technique is suitable for deploying in real-time selective floor-cleaning applications.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

2020 ◽  
Vol 20 (04) ◽  
pp. 2050035
Author(s):  
Sumit Dhariwal ◽  
Sellappan Palaniappan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Error Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Image Normalization ◽  
Novel Approach ◽  
Low Levels ◽  
To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

A graph-based active learning method for classification of remote sensing images

2018 ◽  
Vol 16 (04) ◽  
pp. 1850023
Author(s):  
Lian-Zhi Huo ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
Active Learning ◽  
Image Classification ◽  
Selection Procedure ◽  
Experimental Tests ◽  
Maximization Problem ◽  
Support Vector ◽  
Training Samples

Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity selection procedure is converted to the densest k-subgraph [Formula: see text] maximization problem in graph theory. The [Formula: see text] maximization problem is solved by a greedy algorithm. The proposed technique is compared with competing methods adopted in RS community. Experimental tests are performed on very high resolution (VHR) multispectral and hyperspectral images. Experimental results demonstrate that the proposed technique leads to comparable or even better classification accuracies with respect to competing methods on the two datasets.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Lemon Leaf Disease Detection and Classification using SVM and CNN

2019 ◽  
Vol 8 (4) ◽  
pp. 11485-11488
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Color Space ◽  
Input Image ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Support Vector ◽  
Training Time ◽  
Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

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