scholarly journals Micro-Calcification Classification Analysis in Mammogram Images With aid of Hybrid Technique Analysis

2021 ◽  
Author(s):  
M.C. Shanker ◽  
M. Vadivel
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Median Filter ◽  
Support Vector ◽  
Hybrid Technique ◽  
Scale Invariant ◽  
Hu Moments ◽  
Automated Technique ◽  
Occurrence Matrix ◽  
Mammogram Images

Abstract The main cause of death in women is breast cancer. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and Chimp Optimization Algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).

scholarly journals Gait Recognition Analysis for Human Identification Analysis- A Hybrid Deep Learning Process

2021 ◽  
Author(s):  
Mathivanan B ◽  
Perumal P
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Median Filter ◽  
Gait Recognition ◽  
Human Gait ◽  
Scale Invariant ◽  
Inference System ◽  
Statistical Measures ◽  
Hu Moments

Abstract Gait is an individual biometric behavior which can be detected based on distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, Advanced Deep Belief Neural Network with Black Widow Optimization (ADBNN-BWO) Algorithm is developed to identify the human emotions by human walking style images. This proposed methodology is working based on four stages like pre-processing, feature extraction, feature selection and classification. For the pre-processing, contrast enhancement median filter is used and Hu Moments, GLCM, Fast Scale-invariant feature transform (F-SIFT), in addition skeleton features are used for the feature extraction. To extract the features efficiently, the feature extraction algorithm can be often very essential calculation. After that, feature selection is performed. Then the classification process is done by utilizing the proposed ADBNN-BWO Algorithm. Based on the proposed method, the human gait recognition is achieved which utilized to identify the emotions from the walking style. The proposed method is validated by using the open source gait databases. The proposed method is implemented in MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively.

scholarly journals PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX

2020 ◽  
Vol 3 (1) ◽  
pp. 46-51
Author(s):  
Febri Liantoni ◽  
Agus Santoso
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Median Filter ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Gray Level ◽  
Image Construction ◽  
Occurrence Matrix ◽  
Mammogram Images

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

scholarly journals RESEARCH ON DIFFERENT FEATURE EXTRACTION AND MAMMOGRAM CLASSIFICATION TECHNIQUES

2019 ◽  
pp. 1-3
Author(s):  
Anita Kaklotar
Keyword(s):  
Feature Extraction ◽  
Association Rule ◽  
Support Vector ◽  
Common Disease ◽  
Extraction Techniques ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Mammogram Images

Breast cancer is the primary and the most common disease found among women. Today, mammography is the most powerful screening technique used for early detection of cancer which increases the chance of successful treatment. In order to correctly detect the mammogram images as being cancerous or malignant, there is a need of a classier. With this objective, an attempt is made to analyze different feature extraction techniques and classiers. In the proposed system we rst do the preprocessing of the mammogram images, where the unwanted noise and disturbances in the mammograms are removed. Features are then extracted from the mammogram images using Gray Level Co-Occurrences Matrix (GLCM) and Scale Invariant Feature Transform (SIFT). Finally, the features are classied using classiers like HiCARe (Classier based on High Condence Association Rule Agreements), Support Vector Machine (SVM), Naïve Bayes classier and K-NN Classier. Further we test the images and classify them as benign or malignant class.

scholarly journals CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS

2022 ◽  
Vol 23 (1) ◽  
pp. 187-199
Author(s):  
Suzani Mohamad Samuri ◽  
Try Viananda Nova ◽  
Bahbibi Rahmatullah ◽  
Shir Li Wang ◽  
Z.T Al-Qaysi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
The State ◽  
Classification Model ◽  
Support Vector ◽  
Digital Mammogram ◽  
Grey Level ◽  
Occurrence Matrix ◽  
Mammogram Images

Machine learning has been the topic of interest in research related to early detection of breast cancer based on mammogram images. In this study, we compare the performance results from three (3) types of machine learning techniques: 1) Naïve Bayes (NB), 2) Neural Network (NN) and 3) Support Vector Machine (SVM) with 2000 digital mammogram images to choose the best technique that could model the relationship between the features extracted and the state of the breast (‘Normal’ or ‘Cancer’). Grey Level Co-occurrence Matrix (GLCM) which represents the two dimensions of the level variation gray in the image is used in the feature extraction process. Six (6) attributes consist of contrast, variance, standard deviation, kurtosis, mean and smoothness were computed as feature extracted and used as the inputs for the classification process. The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques. 70% of the data were used as the training data and another 30% used as testing data. The result after ten (10) experiments show that, Support Vector Machine (SVM) gives the most consistent results in correctly classifying the state of the breast as ‘Normal’ or ‘Cancer’, with the accuracy of 99.4%, in training and 98.76% in testing. The SVM classification model has outperformed NN and NB model in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage. ABSTRAK: Pembelajaran mesin telah menjadi topik yang diminati dalam penyelidikan yang berkaitan dengan pengesanan awal kanser payudara berdasarkan imej mamogram. Dalam kajian ini, kami membandingkan hasil prestasi dari tiga (3) jenis teknik pembelajaran mesin: 1) Naïve Bayes (NB), 2) Neural Network (NN) dan 3) Support Vector Machine (SVM) dengan 2000 imej digital mammogram hingga teknik terbaik yang dapat memodelkan hubungan antara ciri yang diekstraksi dan keadaan payudara ('Normal' atau 'Cancer') dapat diperoleh. Grey Level Co-occurrence Matrix (GLCM) yang mewakili dua dimensi variasi tahap kelabu pada gambar digunakan dalam proses pengekstrakan ciri. Enam (6) atribut terdiri dari kontras, varians, sisihan piawai, kurtosis, min dan kehalusan dihitung sebagai fitur yang diekstrak dan digunakan sebagai input untuk proses klasifikasi. Eksperimen telah diulang selama sepuluh (10) kali untuk memeriksa kesesuaian prestasi semua teknik. 70% data digunakan sebagai data latihan dan 30% lagi digunakan sebagai data ujian. Hasil setelah sepuluh (10) eksperimen menunjukkan bahawa, Support Vector Machine (SVM) memberikan hasil yang paling konsisten dalam mengklasifikasikan keadaan payudara dengan betul sebagai 'Normal' atau 'Kanser', dengan akurasi 99.4%, dalam latihan dan 98.76% dalam ujian. Model klasifikasi SVM telah mengungguli model NN dan NB dalam kajian ini, dan ia menunjukkan bahawa SVM adalah pilihan yang baik untuk menentukan keadaan payudara pada peringkat awal.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

scholarly journals Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization

2018 ◽  
Vol 27 (4) ◽  
pp. 681-697
Author(s):  
Lawrence Livingston Godlin Atlas ◽  
Kumar Parasuraman
Keyword(s):  
Particle Swarm Optimization ◽  
Median Filter ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Retinal Hemorrhage ◽  
Scale Invariant ◽  
Swarm Optimization ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.

Sign in / Sign up

Export Citation Format

Share Document