scholarly journals Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Joanna Jaworek-Korjakowska
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Early Stage ◽  
Skin Lesions ◽  
Support Vector ◽  
Svm Classifier ◽  
Stage Of Development ◽  
Vector Machines ◽  
Aided Diagnosis

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification.Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs.Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination.Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

Multi-Class Skin Cancer Detection and Classification Using Hybrid Features Extraction Techniques

2020 ◽  
Vol 10 (10) ◽  
pp. 2466-2472
Author(s):  
Mahnoor Masood ◽  
Khalid Iqbal ◽  
Qasim Khan ◽  
Ali Saeed Alowayr ◽  
Khalid Mahmood Awan ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Early Stage ◽  
Noise Removal ◽  
Support Vector ◽  
Hybrid Features ◽  
Cancer Types ◽  
Biopsy Methods ◽  
Skin Cancer Detection ◽  
Aided Diagnosis

Skin cancer is measured as one of the fatal types of cancer diseases in humans, among numerous kinds of malignancy. Current diagnostic classifications are lacking in finding an effective treatment. The effective and early stage treatment of skin disease can increase the survival rate of patients. Substantial investigative work has been developed to improve computer aided diagnosis system to detect cancer at early stage. However, early detection of skin cancer still requires better accuracy through experiment on digital skin lesion images as a multiclass classification, rather than using biopsy methods. This paper presents an intelligent framework to detect and classify four types of skin cancer. Before classification, noise removal from skin lesion is performed by gaussian filter. Textural and colour features are extracted from skin lesion to detect and classify cancer into four types. Support vector Machine is trained to classify Melanoma, Nevus, Basal and Squamous skin cancer types. Extensive experiments are performed on standard benchmark skin cancer images dataset with an improvement in accuracy of 92.41% after comparison with the well-known methods.

scholarly journals Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

2021 ◽  
Author(s):  
SANTI BEHERA ◽  
PRABIRA SETHY
Keyword(s):  
Support Vector Machine ◽  
Skin Cancer ◽  
Principal Component ◽  
Skin Lesions ◽  
Classification Model ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Feature ◽  
Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

scholarly journals Intelligent Classification Technique for Breast Cancer

2019 ◽  
Vol 8 (6) ◽  
pp. 2313-2316
Keyword(s):  
Breast Cancer ◽  
Developed Countries ◽  
Invasive Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Image Processing Algorithm ◽  
Non Invasive ◽  
Computer Aided ◽  
Aided Diagnosis

Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.

CLASSIFICATION OF GLAUCOMA USING SIMPLIFIED-MULTICLASS SUPPORT VECTOR MACHINE

2019 ◽  
Vol 31 (05) ◽  
pp. 1950039
Author(s):  
S. Renukalatha ◽  
K. V. Suresh
Keyword(s):  
Support Vector Machine ◽  
Early Stage ◽  
Support Vector ◽  
Automatic Assessment ◽  
Hybrid Classification ◽  
Multiclass Support Vector Machine ◽  
Detection And Diagnosis ◽  
High Degree ◽  
Aided Diagnosis

Detection and diagnosis of glaucoma disease of eye fundus images at early stage is very important as this disorder leads to complete loss of vision if ignored. Usually, 80–90% of glaucoma cases are analyzed manually by ophthalmologists. As the manual analysis varies from one expert to other, diagnosis cannot be effective. Hence, there is a need for automatic assessment of glaucoma disease using computer aided diagnosis (CAD). Many researchers have devised several CAD techniques for glaucoma analysis using various classification techniques. However, most of the classifiers are efficient only for two level classification to detect whether disease is glaucoma or not. But, glaucoma disease has several stages and demands multilevel approaches with high degree of classification accuracy. Among several multiclass methods, literature suggests multiclass support vector technique (MSVM) as a better performing statistical classifier. However, many MSVMS suffer from data loss during training phase. To address this issue, a robust hybrid classification approach consisting of Naïve Bayes binary classifier in the first stage and simplified multiclass support vector machine (Sim-MSVM) in the second stage is proposed in this paper.

scholarly journals A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 145
Author(s):  
Hongquan Qu ◽  
Zhanli Fan ◽  
Shuqin Cao ◽  
Liping Pang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Vector Machines ◽  
Full Band ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

CLASSIFICATION OF NORMAL AND KNEE JOINT DISORDER VIBROARTHROGRAPHIC SIGNALS USING MULTIFRACTALS AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 29 (03) ◽  
pp. 1750016 ◽  
Author(s):  
Agastinose Ronickom Jac Fredo ◽  
Thomas Raj Josena ◽  
Rajkumar Palaniappan ◽  
Asaithambi Mythili
Keyword(s):  
Support Vector Machines ◽  
Knee Joint ◽  
Polynomial Function ◽  
Multifractal Spectrum ◽  
Support Vector ◽  
Svm Classifier ◽  
Geometrical Features ◽  
Vector Machines ◽  
Joint Disorder

The development of reliable Computer Aided Diagnosis (CAD) systems would help in the early detection of Knee Joint Disorder (KJD). In this work, normal and KJD vibroarthrographic (VAG) signals are classified using multifractals and Support Vector Machines (SVM). Multifractal dimension [Formula: see text] is calculated from the VAG signals for various [Formula: see text]-values ([Formula: see text]). Geometrical features are calculated from the multifractal spectrum. The dimension of the feature set is reduced using Principal Component Analysis (PCA). The significant features obtained from the multifractal spectrum are fed as the input to the SVM classifier. The accuracy of the classifier is analyzed using kernels such as linear, quadratic, polynomial and Radial Basis Functions (RBF). The results suggest that VAG signals exhibits the multifractal property. The fluctuations in the normal and abnormal signals are well predicted in small scales of segments of time series. The features such as [Formula: see text] and Mean[Formula: see text] are high in abnormal VAG signals. These features give statistically significant values in differentiating the normal and abnormal subjects ([Formula: see text]). The area under the Receiver Operating Characteristic (ROC) curve is high for polynomial function (0.98). The SVM classifier with polynomial function gives 92.13% of accuracy in differentiating the normal and abnormal subjects. The calculation of multifractal spectrum and geometrical features from VAG signals requires optimization of few parameters, easy to compute, computationally inexpensive, and less time consuming. Hence, the CAD system seems to be clinically significant for the classification of normal and KJD subjects.

scholarly journals Classification of Static Security Status Using Multi-Class Support Vector Machines

2012 ◽  
Vol 9 (1) ◽  
pp. 21 ◽  
Author(s):  
S Kalyani ◽  
KS Swarup
Keyword(s):  
Pattern Recognition ◽  
Power Systems ◽  
Misclassification Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Security Assessment ◽  
Classifier Design ◽  
Vector Machines ◽  
Static Security

 This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques. 

scholarly journals Accuracy Assessment & Classification of Keratosis Skin Lesion Images using Feature Extraction & Classification Algorithms-LBP, LDP& HOG

2020 ◽  
Vol 9 (6) ◽  
pp. 397-403
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Skin Diseases ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Epidermal Keratinocytes ◽  
Svm Classifier ◽  
Skin Area ◽  
Seborrheic Keratosis

There are hundreds of human-affected skin diseases. The most severe skin disorders may have identical symptoms, so recognizing the distinctions between them is crucial. People should work closely with a dermatologist to identify and manage every skin disorder and insure it does not impact their lifestyle.Actinic keratosis (AK), that is also classified as solar or senile keratosis; is a pre-malignant crusty, thick skin area. It is a disorder of epidermal keratinocytes, induced by UV radiation upon the skin. While pre-cancerous in nature, they can develop into a form of skin cancer called carcinoma if left unaddressed. The other type of keratosis dealt within this paper is seborrheic keratosis, which are brown or black, thick, wart-like, waxy oval-shaped, slightly raised skin surfaces. The growths aren't damaging. Nevertheless, in some instances it can be impossible to differentiate a seborrheic keratosis from melanoma, which is a very dangerous form of skin cancer.Nevus (or moles) skin lesions are ones which are benign, where it may very rarely turn into melanoma skin cancer. In this article, along with techniques for extracting features (LDP [Local Directional Patterns], LBP [Local Binary Patterns] and HOG [Histogram of Oriented Gradients]),we have used an SVM classifier for the classification of Keratosis and also nevus skin photos. The LBP, LDP and HOG are means to extract features; these images are subsequently used for identification of derived features from these methods or algorithms and classified by the SVM (Support Vector Machine) classifier. For many of the classifications of keratosis and nevus skin images using these algorithms, we have obtained accuracy nearly above 80 %, whereby the LBP system together with the SVM classifier was the most powerful attribute extraction tool of the three with their polynomial kernel type. Using this algorithm-classifier,the main AK and nevus skin lesion images can be detected and diagnosed by the doctors in its early stage itself,thus helping save lives.

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