Ensemble Classifier for Benign-Malignant Mass Classification

2013 ◽  
Vol 3 (1) ◽  
pp. 66-77
Author(s):  
Muhammad Hussain
Keyword(s):  
Imaging Modality ◽  
Majority Vote ◽  
Ensemble Classifier ◽  
Final Decision ◽  
Classification Rate ◽  
Cad System ◽  
Gabor Features ◽  
The Masses ◽  
Micro Structures ◽  
Malignant Mass

Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested (ROIs), which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard problem because the texture micro-structures of benign and malignant masses have close resemblance. In this paper, a method for classifying mass ROIs into benign and malignant masses is presented. The key idea of the proposal is to build an ensemble classifier that employs Gabor features, consults different experts (classifiers) and takes the final decision based on majority vote. The system is evaluated on 512 (256 benign+256 malignant) mass ROIs extracted from mammograms of DDSM database. The ensemble classifier improves the classification rate for the problem of the discrimination of benign and malignant masses to 90.64%. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods.

scholarly journals Inter-observer agreement on the evaluation of automated breast volume scanner for breast masses

2022 ◽  
pp. 1-11
Author(s):  
Yingchun Liu ◽  
Lin Chen ◽  
Jia Zhan ◽  
Xuehong Diao ◽  
Yun Pang ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Observer Agreement ◽  
Breast Volume ◽  
Imaging Data ◽  
Predictive Values ◽  
Breast Masses ◽  
Cutoff Value ◽  
Automated Breast Volume Scanner ◽  
The Masses ◽  
Sensitivity Specificity

Objective: To explore inter-observer agreement on the evaluation of automated breast volume scanner (ABVS) for breast masses. Methods: A total of 846 breast masses in 630 patients underwent ABVS examinations. The imaging data were independently interpreted by senior and junior radiologists regarding the mass size ([Formula: see text][Formula: see text]cm, [Formula: see text][Formula: see text]cm and total). We assessed inter-observer agreement of BI-RADS lexicons, unique descriptors of ABVS coronal planes. Using BI-RADS 3 or 4a as a cutoff value, the diagnostic performances for 331 masses with pathological results in 253 patients were assessed. Results: The overall agreements were substantial for BI-RADS lexicons ([Formula: see text]–0.779) and the characteristics on the coronal plane of ABVS ([Formula: see text]), except for associated features ([Formula: see text]). However, the overall agreement was moderate for orientation ([Formula: see text]) for the masses [Formula: see text][Formula: see text]cm. The agreements were substantial to be perfect for categories 2, 3, 4, 5 and overall ([Formula: see text]–0.918). However, the agreements were moderate to substantial for categories 4a ([Formula: see text]), 4b ([Formula: see text]), and 4c ([Formula: see text]), except for category 4b of the masses [Formula: see text][Formula: see text]cm ([Formula: see text]). Moreover, for radiologists 1 and 2, there were no significant differences in sensitivity, specificity, accuracy, positive and negative predictive values with BI-RADS 3 or 4a as a cutoff value ([Formula: see text] for all). Conclusion: ABVS is a reliable imaging modality for the assessment of breast masses with good inter-observer agreement.

Medical Image Classification Using the Discriminant Power Analysis (DPA) of Discrete Cosine Transform (DCT) Coefficients

2021 ◽  
Author(s):  
Nasser Edinne Benhassine ◽  
Abdelnour Boukaache ◽  
Djalil Boudjehem
Keyword(s):  
Image Classification ◽  
Discrete Cosine Transform ◽  
Input Image ◽  
Final Decision ◽  
K Nearest Neighbors ◽  
Cosine Transform ◽  
Cad System ◽  
Selection Step ◽  
Analysis Society ◽  
Artificial Neural Network Ann

Medical imaging systems are very important in medicine domain. They assist specialists to make the final decision about the patient’s condition, and strongly help in early cancer detection. The classification of mammogram images represents a very important operation to identify whether the breast cancer is benign or malignant. In this chapter, we propose a new computer aided diagnostic (CAD) system, which is composed of three steps. In the first step, the input image is pre-processed to remove the noise and artifacts and also to separate the breast profile from the pectoral muscle. This operation is a difficult task that can affect the final decision. For this reason, a hybrid segmentation method using the seeded region growing (SRG) algorithm applied on a localized triangular region has been proposed. In the second step, we have proposed a features extraction method based on the discrete cosine transform (DCT), where the processed images of the breast profiles are transformed by the DCT where the part containing the highest energy value is selected. Then, in the feature’s selection step, a new most discriminative power coefficients algorithm has been proposed to select the most significant features. In the final step of the proposed system, we have used the most known classifiers in the field of the image classification for evaluation. An effective classification has been made using the Support Vector Machines (SVM), Naive Bayes (NB), Artificial Neural Network (ANN) and k-Nearest Neighbors (KNN) classifiers. To evaluate the efficiency and to measure the performances of the proposed CAD system, we have selected the mini Mammographic Image Analysis Society (MIAS) database. The obtained results show the effectiveness of the proposed algorithm over others, which are recently proposed in the literature, whereas the new CAD reached an accuracy of 100%, in certain cases, with only a small set of selected features.

scholarly journals Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets

2018 ◽  
Vol 7 (4.15) ◽  
pp. 281
Author(s):  
Bibhuprasad Sahu ◽  
Sujata Dash ◽  
Sachi Nandan Mohanty ◽  
Saroj Kumar Rout
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Early Stage ◽  
Breast Cancer Dataset ◽  
Classification Rate ◽  
Cancer Dataset ◽  
Cancer Data ◽  
Cad System ◽  
Result Analysis ◽  
Sensitivity Specificity

Every disease is curable if a little amount of human effort is applied for early diagnosis. The death rate in world increases day by day as patient fail to detect it before it becomes chronic. Breast cancer is curable if detection is done at early stage before it spread across all part of body. Now-a-days computer aided diagnosis are automated assistance for the doctors to produce accurate prediction about the stage of disease. This study provided CAD system for diagnosis of breast cancer. This method uses Neural Network (NN) as a classifier model and PCA/LDA for dimension reduction method to attain higher classification rate. Multiple layers of neural network are applied to classify the breast cancer data. This system experiment done on Wisconsin breast cancer dataset (WBCD) from UCI repository. The dataset is divided into 2 parts train and test. With the result of accuracy, sensitivity, specificity, precision and recall the performance can be measured. The results obtained are this study is 97% using ANN and PCA-ANN, which is better than other state-of-art methods. As per the result analysis this system outperformed then the existing system.  

BIDIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION OF FUNCTIONAL BIOMEDICAL IMAGES

2014 ◽  
Vol 06 (01) ◽  
pp. 1450004 ◽  
Author(s):  
A. NEUBAUER ◽  
A. M. TOMÉ ◽  
A. KODEWITZ ◽  
J. M. GÓRRIZ ◽  
C. G. PUNTONET ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Imaging Modality ◽  
Ensemble Empirical Mode Decomposition ◽  
Cad System ◽  
Positron Emission ◽  
Mode Decomposition ◽  
Functional Images ◽  
Human Brains ◽  
Aided Diagnosis ◽  
Potential Use

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.

scholarly journals Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Alaa Khudhair Abbas ◽  
Ali Khalil Salih ◽  
Harith A. Hussein ◽  
Qasim Mohammed Hussein ◽  
Saba Alaa Abdulwahhab
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Majority Vote ◽  
Ensemble Classifier ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classification Model ◽  
Media Messages ◽  
Individual Classifier ◽  
The Individual

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

scholarly journals Multi Deep Learning to Diagnose COVID-19 in Lung X-Ray Images with Majority Vote Technique

2020 ◽  
Vol 13 (6) ◽  
pp. 560-568
Author(s):  
Ike Fibriani ◽  
◽  
Widjonarko Widjonarko ◽  
Aris Prasetyo ◽  
Angga Raharjo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Health System ◽  
Majority Vote ◽  
Learning System ◽  
Final Decision ◽  
Initial Screening ◽  
X Ray ◽  
Rate Of Spread ◽  
Proposed Model

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.

scholarly journals MRI characteristics of intracranial masses in the paediatric population of KwaZulu-Natal: A neuroimaging-based study

2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Nompumelelo P. Gumede ◽  
Sithembiso M. Langa ◽  
Basil Enicker
Keyword(s):  
Imaging Modality ◽  
Paediatric Population ◽  
Brain Scans ◽  
Primary Brain Tumours ◽  
Imaging Characteristics ◽  
Kwazulu Natal ◽  
The Common ◽  
The Mean ◽  
The Masses ◽  
Mri Characteristics

Background: MRI is the imaging modality of choice for the assessment of intracranial masses in children. Imaging is vital in planning further management.Objectives: The purpose of this study was to describe the common intracranial masses and their imaging characteristics in the paediatric population referred to Inkosi Albert Luthuli Central Hospital for MRI of the brain.Method: We retrospectively reviewed the medical records of paediatric patients (aged from birth to 18 years) who underwent MRI investigations for intracranial masses between January 2010 and December 2016.Results: A total of 931 MRI brain scans were performed. One hundred and seven scans met the inclusion criteria, of which 92 were primary brain tumours and 15 were inflammatory masses. The majority were females (56%). The mean age was 12 ± 4.52 (range of 3–18 years). The most common presenting symptom was seizures (70/107, 65.4%). We categorised the masses according to supra- and infratentorial compartments. The most common site for masses was the supratentorial compartment (n = 56, 52%). The most common masses in the supratentorial compartment were craniopharyngiomas (14/45, 31.1%), whilst in the infratentorial compartment, the most common masses were medulloblastomas (24/47, 51.1%).Conclusion: In our series, the supratentorial compartment was the commonest site for intracranial masses. The most common tumour in the infratentorial compartment was medulloblastoma. This information is vital in formulating differential diagnoses of intracranial masses.

scholarly journals Representative Committees of Peers

2021 ◽  
Vol 71 ◽  
pp. 401-429
Author(s):  
Reshef Meir ◽  
Fedor Sandomirskiy ◽  
Moshe Tennenholtz
Keyword(s):  
Social Welfare ◽  
Social Cost ◽  
Majority Vote ◽  
Preference Profile ◽  
Final Decision ◽  
Worst Case ◽  
Optimal Rule ◽  
The Social ◽  
Small Committee ◽  
Elected Representatives

A population of voters must elect representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. The disutility of a voter is proportional to the fraction of issues, where his preferences disagree with the decision. While an issue-by-issue vote by all voters would maximize social welfare, we are interested in how well the preferences of the population can be approximated by a small committee. We show that a k-sortition (a random committee of k voters with the majority vote within the committee) leads to an outcome within the factor 1+O(1/√ k) of the optimal social cost for any number of voters n, any number of issues m, and any preference profile. For a small number of issues m, the social cost can be made even closer to optimal by delegation procedures that weigh committee members according to their number of followers. However, for large m, we demonstrate that the k-sortition is the worst-case optimal rule within a broad family of committee-based rules that take into account metric information about the preference profile of the whole population.

scholarly journals Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammad S. Islam ◽  
Khondaker A. Mamun ◽  
Hai Deng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Local Field ◽  
Local Field Potentials ◽  
Ensemble Classifier ◽  
Majority Voting ◽  
Final Decision ◽  
Field Potentials ◽  
Left And Right ◽  
Deep Brain

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson’s disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about0.729±0.16for decoding movement from the resting state and about0.671±0.14for decoding left and right visually cued movements.

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