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 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 Diagnosis and Classification of Brain Tumor

2019 ◽  
Vol 9 (1) ◽  
pp. 1622-1630
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Early Detection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Detection And Identification

The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images

scholarly journals Detecting Types of Sleep Apneas Through Nonlinear Features of Electrocardiogram and Electroencephalogram Signals

2021 ◽  
Author(s):  
Shaghayegh Saghafi ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Sleep Apnea ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Majority Voting ◽  
Support Vector ◽  
Svm Classifier ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nonlinear Features

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important. Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron. Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively. Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

scholarly journals Skin Disease Detection: Machine Learning vs Deep Learning

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Amiya Bhaumik ◽  
Sandeep Poddar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Process ◽  
Skin Disease ◽  
Nearest Neighbor ◽  
Network Models ◽  
Support Vector ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Neural Network Models

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

scholarly journals An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5916
Author(s):  
Tariq Mahmood ◽  
Jianqiang Li ◽  
Yan Pei ◽  
Faheem Akhtar ◽  
Azhar Imran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fusion Algorithm ◽  
Enhancement Method ◽  
Experimental Findings

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

scholarly journals Breast Cancer Prediction Using Machine Learning

2020 ◽  
pp. 278-284
Author(s):  
Gaurav Singh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbor ◽  
Cancer Dataset ◽  
Implementation Phase ◽  
Machine Learning Classification

Breast cancer may be a prevalent explanation for death, and it's the sole sort of cancer that's widespread among women worldwide. The prime objective of this paper creates the model for predicting breast cancer using various machine learning classification algorithms like k Nearest Neighbor (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Gaussian Naive Bayes (NB). And furthermore, assess and compare the performance of the varied classifiers as far as accuracy, precision, recall, f1-Score, and Jaccard index. The breast cancer dataset is publicly available on the UCI Machine Learning Repository and therefore the implementation phase dataset is going to be partitioned as 80% for the training phase and 20% for the testing phase then apply the machine learning algorithms. k Nearest Neighbors achieved a significant performance in respect of all parameters.

A Robust Sleep Apnea-hypopnea Syndrome Automated Detection Method Based on Fuzzy Entropy Using Single Lead-EEG Signals

2021 ◽  
Author(s):  
Yao Wang ◽  
Tianshun Yang ◽  
Siyu Ji ◽  
Xiaohong Wang ◽  
Huiquan Wang ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Nearest Neighbor ◽  
Study Data ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Respiratory Events ◽  
Complex Process ◽  
Hypopnea Syndrome

Abstract Sleep apnea-hypopnea syndrome is a relatively common disease, characterized by a repetitive reduction or cessation of respiratory airflow, which seriously affects sleep quality and in the long term, can lead to heart disease, hypertension, diabetes, and stroke. Polysomnography is currently the standard method for diagnosing apnea/hypopnea. However, accurate diagnosis can be difficult due to the complex process of multi-signals acquisition in polysomnography. Instead, this paper presents a novel automated fuzzy entropy-based method for detecting apnea/hypopnea using single-lead electroencephalogram signals. The method consists of four steps: (1) The electroencephalogram signals corresponding to respiratory events are partitioned into five sub-bands according to frequency; (2) Features of fuzzy entropy in each sub-band are extracted; (3) The extracted features are evaluated using statistical methods; (4) The features are classified using a classifier, such as the support vector machine, k-nearest neighbor, and random forest algorithms. In this study, data were obtained from a total of 55 subjects with sleep apnea-hypopnea syndrome from both a public and clinical database. The experimental results indicated that all of the selected metrics, including accuracy, sensitivity, and specificity were close to or above 90% for both publicly available and clinical data. Moreover, this approach is sensitive to all types of sleep apnea/hypopnea, an important aspect that is rarely explicitly discussed in the literature.

scholarly journals Expert cancer model using supervised algorithms with a LASSO selection approach

2021 ◽  
Vol 11 (3) ◽  
pp. 2631
Author(s):  
Pronab Ghosh ◽  
Asif Karim ◽  
Syeda Tanjila Atik ◽  
Saima Afrin ◽  
Mohd. Saifuzzaman
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Disease Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cancer Disease ◽  
Critical Issues ◽  
Selection Operator ◽  
Fold Cross Validation

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

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