scholarly journals Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1870
Author(s):  
Yaghoub Pourasad ◽  
Esmaeil Zarouri ◽  
Mohammad Salemizadeh Parizi ◽  
Amin Salih Mohammed
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Premature Deaths ◽  
Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

scholarly journals Comparative analysis on bayesian classification for breast cancer problem

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Wan Nor Liyana Wan Hassan Ibeni ◽  
Mohd Zaki Mohd Salikon ◽  
Aida Mustapha ◽  
Saiful Adli Daud ◽  
Mohd Najib Mohd Salleh
Keyword(s):  
Breast Cancer ◽  
Bayesian Networks ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Likelihood Estimation ◽  
Predictive Distribution ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.

scholarly journals Novel Mathematical Model of Breast Cancer Diagnostics Using an Associative Pattern Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 136 ◽  
Author(s):  
Raúl Santiago-Montero ◽  
Humberto Sossa ◽  
David A. Gutiérrez-Hernández ◽  
Víctor Zamudio ◽  
Ignacio Hernández-Bautista ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Cancer Diagnostics ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Breast Cancer Death ◽  
Positron Emission ◽  
The Government

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

scholarly journals Classification Methods for Airborne Disease Spores from Greenhouse Crops Based on Multifeature Fusion

2020 ◽  
Vol 10 (21) ◽  
pp. 7850 ◽  
Author(s):  
Yafei Wang ◽  
Xiaoxue Du ◽  
Guoxin Ma ◽  
Yong Liu ◽  
Bin Wang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Fungal Spores ◽  
Recall Rate ◽  
Support Vector ◽  
Morphological Operations ◽  
K Nearest Neighbor ◽  
Image Preprocessing ◽  
Greenhouse Crops ◽  
Svm Model ◽  
Airborne Disease

Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.

scholarly journals A Heterogeneous Learning Framework for Over-the-Top Consumer Analysis Reflecting the Actual Market Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 4783
Author(s):  
Jaeun Choi ◽  
Yongsung Kim
Keyword(s):  
High Performance ◽  
Nearest Neighbor ◽  
Service Providers ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Wireless Internet ◽  
K Nearest Neighbor ◽  
Market Environment ◽  
Conventional Methods ◽  
Over The Top

The over-the-top (OTT) market for media consumption over wired and wireless Internet is growing. It is, therefore, crucial that service providers and carriers participating in the OTT market analyze consumer traffic for pricing, service delivery, infrastructure investments, etc. The OTT market has many consumer groups, but the proportion of users is not consistent in each. Furthermore, as multimedia consumption has increased owing to the COVID-19 epidemic, the OTT market has changed rapidly. If this is not reflected, the analysis will not be accurate. Therefore, we propose a framework that can classify consumers well based on actual OTT market environment conditions. First, by applying our proposed conditional probability-based method to basic machine learning techniques, such as support vector machine, k-nearest neighbor, and decision tree, we can improve the classification performance, even for an imbalanced OTT consumer distribution. Then, it is possible to analyze the changing consumer trends by dynamically retraining the incoming OTT consumer data. Conventional methods result in low classification accuracy in low-number classes, but our method shows an improvement of 5.3–19.2% based on recall. Moreover, conventional methods have shown large fluctuations in performance as the OTT market environment has changed, but our framework consistently maintains high performance.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

POLYNOMIAL NETWORKS VERSUS OTHER TECHNIQUES IN TEXT CATEGORIZATION

2008 ◽  
Vol 22 (02) ◽  
pp. 295-322 ◽  
Author(s):  
MAYY M. AL-TAHRAWI ◽  
RAED ABU ZITAR
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
New Techniques ◽  
Vector Machines ◽  
Benchmark Datasets ◽  
Automatic Text

Many techniques and algorithms for automatic text categorization had been devised and proposed in the literature. However, there is still much space for researchers in this area to improve existing algorithms or come up with new techniques for text categorization (TC). Polynomial Networks (PNs) were never used before in TC. This can be attributed to the huge datasets used in TC, as well as the technique itself which has high computational demands. In this paper, we investigate and propose using PNs in TC. The proposed PN classifier has achieved a competitive classification performance in our experiments. More importantly, this high performance is achieved in one shot training (noniteratively) and using just 0.25%–0.5% of the corpora features. Experiments are conducted on the two benchmark datasets in TC: Reuters-21578 and the 20 Newsgroups. Five well-known classifiers are experimented on the same data and feature subsets: the state-of-the-art Support Vector Machines (SVM), Logistic Regression (LR), the k-nearest-neighbor (kNN), Naive Bayes (NB), and the Radial Basis Function (RBF) networks.

Fuzzy classification of OpenCL programs targeting heterogeneous systems

2020 ◽  
Vol 39 (5) ◽  
pp. 7189-7202
Author(s):  
Ahmad Al-Zoubi ◽  
Konstantinos Tatas ◽  
Costas Kyriacou
Keyword(s):  
High Performance ◽  
Energy Savings ◽  
Nearest Neighbor ◽  
Heterogeneous Systems ◽  
Substantial Improvement ◽  
Fuzzy Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Bayes Network ◽  
Time Operation

Heterogeneous systems featuring multiple kinds of processors are becoming increasingly attractive due to their high performance and energy savings over their homogeneous counterparts. With the OpenCL as a unified programming language providing program portability across different types of accelerators, finding the best task-to-device mapping will be the key to achieve such a high performance. We introduce in this work the design of a fuzzy logic classifier and the evaluation of its performance in classifying OpenCL workloads in a CPU-GPU-FPGA heterogeneous environment based on carefully analyzed kernel features. The classifier is designed as part of a scheduling scheme. Results demonstrate substantial improvement in accuracy when compared to other classifiers such as the K-Nearest- Neighbor (KNN), Support-Vector-Machine (SVM), Random-Forest (RF), Naïve-Bayes (NB) and the Bayes-Network (BN) with low computational complexity, facilitating run-time operation.

scholarly journals Big Data-oriented Wheel Position and Geometry Calculation for Cutting Tool Groove Manufacturing based on AI Algorithms

2021 ◽  
Author(s):  
Li Guochao ◽  
Zhigang Liu ◽  
Jie Lu ◽  
Honggen Zhou ◽  
Li Sun
Keyword(s):  
Big Data ◽  
High Performance ◽  
Nearest Neighbor ◽  
Cutting Tools ◽  
Support Vector ◽  
Grinding Wheel ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Resource ◽  
Grinding Machine

Abstract Groove is a key structure of high-performance integral cutting tools. It has to be manufactured by 5-axis grinding machine due to its complex spatial geometry and hard materials. The crucial manufacturing parameters (CMP) are grinding wheel positions and geometries. However, it is a challenging problem to solve the CMP for the designed groove. The traditional trial-and-error or analytical methods have defects such as time-consuming, limited-applying and low accuracy. In this study, the problem is translated into a multiple output regression model of groove manufacture (MORGM) based on the big data technology and AI algorithms. The input are 34 groove geometry features and the output are 5 CMP. Firstly, two groove machining big data sets with different range are established, each of which is includes 46656 records. They are used as data resource for MORGM. Secondly, 7 AI algorithms, including linear regression, k nearest-neighbor regression, decision trees, random forest regression, support vector regression and ANN algorithms are discussed to build the model. Then, 28 experiments are carried out to test the big data set and algorithms. Finally, the best MORGM is built by ANN algorithm and the big data set with a larger range. The results show that CMP can be calculated accurately and conveniently by the built MORGM.

scholarly journals Breast cancer detection using thermal images

2020 ◽  
Vol 9 (3) ◽  
pp. 692
Author(s):  
Amany M. Reda Hawas ◽  
Abeer Twakol Khalil ◽  
El Said Marzouk
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Good Tool ◽  
Thermal Images ◽  
Mortality And Morbidity ◽  
Elapsed Time

Breast cancer is a common disease, accurate and early detection of breast cancer is very important to reduce the mortality and morbidity. Previous studies expose that thermography is a good tool for early detection of the breast cancer. In this paper, a new automatic system will be introduced for the early detection of the breast cancer using thermal images and distinguishing between normal and abnormal breasts. The proposed system is based on combining textural features and histogram of oriented gradients and bag of thermal breast images and then classifying those using three different classifiers: (i) Support vector machine; (ii) Decision tree, and k-Nearest Neighbor. This proposed system provides an automatic classification of the breast cancer using image analysis accurately in low elapsed time. Experimental results showed that cubic SVM has a maximum accuracy of 98.9%, a sensitivity of 98.9%, and a specificity of 99%. When comparing the proposed system with the relevant systems, it’s approved to be more accurate with low elapsed time in learning and testing phase that can help the clinicians in the automatic diagnosis of the breast cancer.   

scholarly journals Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

2017 ◽  
Vol 6 (3) ◽  
pp. 50
Author(s):  
Nanda S. ◽  
Sukumar M.
Keyword(s):  
Support Vector Machine ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Textural Features ◽  
Ultrasound Images ◽  
K Nearest Neighbor ◽  
Benign Nodules

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

Sign in / Sign up

Export Citation Format

Share Document