Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1175-1187
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1328-1340
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2017 ◽  
Vol 6 (2) ◽  
pp. 18-32
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

scholarly journals Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 290 ◽  
Author(s):  
Chenhao Wu ◽  
Jiguang Yue ◽  
Li Wang ◽  
Feng Lyu
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Wavelet Decomposition ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Principal Component ◽  
Excellent Performance ◽  
Feature Vectors ◽  
Parseval’S Theorem

This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy.

scholarly journals Applying WPD and SVD to Classification of EM Wave Induced by Partial Discharge in Power Transformer

2013 ◽  
Vol 64 (4) ◽  
pp. 222-229 ◽  
Author(s):  
Xu Zhao ◽  
Yong-Hong Cheng ◽  
Yong-Peng Meng ◽  
Michael G. Danikas
Keyword(s):  
Power Transformer ◽  
Wavelet Packet ◽  
Partial Discharge ◽  
Training Data ◽  
Decomposition Level ◽  
Frequency Information ◽  
Testing Data ◽  
The Right ◽  
Wave Induced

Partial discharge (PD) current is an impulse signal at nanosecond level, which can generate electromagnetic (EM) wave containing broadband frequency information. The frequency band of EM signal is from MHz up to GHz. Due to different PD patterns, impulse currents with different shapes induce different EM waves containing different frequency information. Therefore, using the features extracted from frequency domain of EM signals, the classification of PD patterns can be effectively got. It is good to use wavelet or wavelet packet decomposition to select features. However, if the decomposition level is too shallow to find enough effective features, it cannot group the EM signals to the right pattern. On the contrary, although it is easier to find features to distinguish the PD pattern if the decomposition level is deep, there will be a lot of redundancy variables and it is hard to select features among so many variables. In this paper, a method is presented, which selected features in the whole decomposition tree instead of selecting among the leaf node of the tree, because more potential features can be found in the whole tree. With the present method, it is possible not only to get enough features, but also to eliminate the redundancy variables effectively. In order to validate the method, large EM signals from four PD patterns in a power transformer are acquired as the training data and testing data for feature selection and classification, and three common classification methods are introduced to classify the PD patterns using the features selected by the method. Most of the classification results are satisfactory indicating that the proposed method is effective.

May-Thurner syndrome – Are we aware enough?

VASA ◽  
2019 ◽  
Vol 48 (5) ◽  
pp. 381-388 ◽  
Author(s):  
Katalin Mako ◽  
Attila Puskas
Keyword(s):  
Contraceptive Use ◽  
Iliac Vein ◽  
Common Iliac Vein ◽  
Vein Thrombosis ◽  
Compression Syndrome ◽  
Threatening Condition ◽  
Life Threatening ◽  
Iliac Vein Compression ◽  
Iliac Vein Compression Syndrome ◽  
The Right

Summary. Iliac vein compression syndrome (May-Thurner syndrome – MTS) is an anatomically variable clinical condition in which the left common iliac vein is compressed between the right common iliac artery and the underlying spine. This anatomic variant results in an increased incidence of left iliac or iliofemoral vein thrombosis. It predominantly affects young women in the second or third decades of life with preponderance during pregnancy or oral contraceptive use. Although MTS is rare, its true prevalence is underestimated but it can be a life-threatening condition due to development of pulmonary embolism (PE). In this case based review the authors present three cases of MTS. All patients had been previously confirmed with PE, but despite they were admitted to hospital, diagnosed and correctly treated for PE and investigated for thrombophilia, the iliac vein compression syndrome was not suspected or investigated. With this presentation the authors would like to emphasize that MTS is mostly underdiagnosed, and it needs to be ruled out in left iliofemoral vein thrombosis in young individuals.

Awareness and Decision Making in Professional Ethics

2000 ◽  
Vol 5 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Ronny Swain
Keyword(s):  
Decision Making ◽  
Professional Ethics ◽  
Ethical Decision Making ◽  
Code Of Ethics ◽  
Professional Autonomy ◽  
Ethical Awareness ◽  
Ethical Decisions ◽  
The Right ◽  
Aid Decision

The paper describes the development of the 1998 revision of the Psychological Society of Ireland's Code of Professional Ethics. The Code incorporates the European Meta-Code of Ethics and an ethical decision-making procedure borrowed from the Canadian Psychological Association. An example using the procedure is presented. To aid decision making, a classification of different kinds of stakeholder (i.e., interested party) affected by ethical decisions is offered. The author contends (1) that psychologists should assert the right, which is an important aspect of professional autonomy, to make discretionary judgments, (2) that to be justified in doing so they need to educate themselves in sound and deliberative judgment, and (3) that the process is facilitated by a code such as the Irish one, which emphasizes ethical awareness and decision making. The need for awareness and judgment is underlined by the variability in the ethical codes of different organizations and different European states: in such a context, codes should be used as broad yardsticks, rather than precise templates.

Clinical Case: Complications after Endovascular Treatment of Intracranial Aneurysm in a Patient after Subarachnoid Hemorrhage

2020 ◽  
Vol 8 (1) ◽  
pp. 9-15
Author(s):  
Petrov Nikolay ◽  
◽  
Marinova R. ◽  
Odiseeva Ev.
Keyword(s):  
Intracranial Aneurysm ◽  
Endovascular Treatment ◽  
Clinical Case ◽  
Endovascular Coiling ◽  
Published Data ◽  
Military Medical Academy ◽  
Life Threatening ◽  
Arachnoid Hemorrhage ◽  
The Right ◽  
Brain Artery

Abstract: Intracranial aneurysm is one of the most common neurovascular complications. During the recent years the accepted treatment of enraptured cranial aneurysm is noninvasive endovascular coiling. This technique is modern but it is not without complications which can be serious and life-threatening. A clinical case of a patient admitted to the ICU of Military Medical Academy - Sofia with sub arachnoid hemorrhage is described. After a positive clinical course, the check-up magnetic resonance showed intracranial aneurism of the right carotid artery. The patient underwent angiographic endovascular treatment. Vasospasm of the middle and right brain artery and thrombosis were detected during the procedure. Attempt of thromboaspiration was made without success. This article reviews published data on broad-spectrum researches concerning complications of endovascular coiling of intracranial aneurysms and the ways to prevent and reduce them.

Experiences in Water Resources Management in Developed Regions

1987 ◽  
Vol 19 (9) ◽  
pp. 217-223
Author(s):  
Leo Weaver ◽  
Peter A. Tennant
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Engineering Application ◽  
Resource Planning ◽  
Developed Countries ◽  
Historical Background ◽  
Science And Engineering ◽  
Resources Management ◽  
Quality Of Water ◽  
The Right

The science and engineering of water resources in developed countries has undergone profound changes in less than a professional lifetime. This has radically influenced education and research and essentially revolutionized water resource planning. Fundamental uses of water to fulfill society's needs continue. These include domestic and industrial supplies, waterways for transport, fire protection, waste carriage, recreation, irrigation, and in general, an adequate quantity and quality of water in the right place at the right time to fulfill human and aquatic needs. The complexities of water resources no longer may be measured largely in terms of science and engineering challenges alone, but must include economics sensitive to the political and social sciences. What has evolved is what is now best described as water management. This paper reviews the evolvement of water resources management in developed counties in terms of historical background, and national policies and institutions, particularly as this evolvement impacts scientific research and engineering application.

scholarly journals Bilateral Phyllodes Giant Tumor. A Case Report Analyzed by Array-CGH

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 825
Author(s):  
Francesco Fortarezza ◽  
Federica Pezzuto ◽  
Gerardo Cazzato ◽  
Clelia Punzo ◽  
Antonio d’Amati ◽  
...  
Keyword(s):  
Array Cgh ◽  
Phyllodes Tumor ◽  
Molecular Classification ◽  
Left Breast ◽  
Comparative Genomic ◽  
Fish Analysis ◽  
Molecular Tests ◽  
Giant Tumor ◽  
Metastatic Risk ◽  
The Right

The breast phyllodes tumor is a biphasic tumor that accounts for less than of 1% of all breast neoplasms. It is classified as benign, borderline, or malignant, and can mimic benign masses. Some recurrent alterations have been identified. However, a precise molecular classification of these tumors has not yet been established. Herein, we describe a case of a 43-year-old woman that was admitted to the emergency room for a significant bleeding from the breast skin. A voluminous ulcerative mass of the left breast and multiple nodules with micro-calcifications on the right side were detected at a physical examination. A left total mastectomy and a nodulectomy of the right breast was performed. The histological diagnosis of the surgical specimens reported a bilateral giant phyllodes tumor, showing malignant features on the left and borderline characteristics associated with a fibroadenoma on the right. A further molecular analysis was carried out by an array-Comparative Genomic Hybridization (CGH) to characterize copy-number alterations. Many losses were detected in the malignant mass, involving several tumor suppressor genes. These findings could explain the malignant growth and the metastatic risk. In our study, genomic profiling by an array-CGH revealed a greater chromosomal instability in the borderline mass (40 total defects) than in the malignant (19 total defects) giant phyllodes tumor, reflecting the tumor heterogeneity. Should our results be confirmed with more sensitive and specific molecular tests (DNA sequencing and FISH analysis), they could allow a better selection of patients with adverse pathological features, thus optimizing and improving patient’s management.

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