scholarly journals Severity Analysis of Cervical Cancer in Pap Smear Images by using EEETCM, ERSTCM & CFE method based Texture Features and Hybrid Kernel based Support Vector Machine Classifier.

2016 ◽  
Vol 4 (11) ◽  
pp. 2751-2764
Author(s):  
S. Athinarayanan ◽  
◽  
Dr.M.V. Srinath ◽  
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Pap Smear ◽  
Texture Features ◽  
Support Vector ◽  
Hybrid Kernel

scholarly journals Nominated Texture Based Cervical Cancer Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Edwin Jayasingh Mariarputham ◽  
Allwin Stephen
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Dynamic Range ◽  
Relative Size ◽  
Texture Features ◽  
Relative Displacement ◽  
University Hospital ◽  
Public Image ◽  
Support Vector ◽  
First Four Moments

Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.

Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2558-2565
Author(s):  
K. Kavinkumar ◽  
T. Meeradevi
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector Machine Classifier ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classification is done by training SVM using the texture features from the hybrid and efficient feature extraction technique.The accuracy of the classification is calculated and the SVM classifier training individual type of texture features and also with combined texture features and the performance is analyzed.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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