AUTOMATED SYSTEM FOR CLASSIFYING BREAST DISEASES BY X-RAY MAMMOGRAPHY

2019 ◽  
Vol 48 (4) ◽  
pp. 10-24
Author(s):  
A.R. Dabagov ◽  
◽  
I.A. Malyutina ◽  
D.S. Kondrashov ◽  
V.V. Serebrovsky ◽  
...  
Keyword(s):  
Automated System ◽  
Breast Diseases ◽  
X Ray

Features hormonal hemostasis in patients with benign hyper-plastic processes of breasts

2016 ◽  
pp. 72-74 ◽  
Author(s):  
M. Makarenko ◽  
◽  
D. Govsieiev ◽  
O. Gromova ◽  
L. Martynova ◽  
...  
Keyword(s):  
Luteal Phase ◽  
Reproductive Organs ◽  
Breast Diseases ◽  
Hormonal Changes ◽  
Morphological Investigation ◽  
Fibrocystic Disease ◽  
X Ray ◽  
Pelvic Organs ◽  
Hormone Research ◽  
Gynecological Diseases

The objective: to study the incidence of gynecological diseases, clinical and hormonal parameters of the menstrual cycle in patients with benign hyper-plastic processes of breasts. Patients and methods. 65 women with various forms of mastitis were investigated. The following investigations were conducted: mammologistic and gynecological investigation, mommologistic X-ray investigation, ultrasound of breasts and of the pelvic organs, endometrial aspiration biopsy that was followed by cytology; when it was necessary the diagnostic laparoscopy, colposcopy, hysteroscope with curettage and morphological investigation of the endometrium, hormone research and rectal temperature measurements were conducted. Results. The frequency of the benign breast diseases was set: fibrocystic disease of breast – 32 women (49.2±6.20%), fibrous of breast – 16 women (24.6±5.34%), nodular of breasts – 8 women (12.3±4.07%), fibroadenoma – 6 women (9.2±3.59%), nodular disease of breasts on the background of fibroid changes – 3 women (4.6±2.60%). All in all, 96.9±2.14% of the patients had any gynecological diseases. Thus, the average age of the ‘debut’ of mastitis was 31.4±1.09 years; the hyper-plastic processes in the uterus was 35.2±1.17 years. Anovulation was detected in 17 (47.2±8.3%) patients, the lack of the luteal phase (NLF) was detected in 11 (30.6±7.6) patients. Conclusions. Identified hormonal changes are typical for patients with the hyper-plastic processes of the reproductive organs with different localization (breasts, uterus, ovaries). Due to the commonality of the hormone changes in most cases mastitis is combined with the various gynecological diseases (96.9±2.14%). Key words: gynecological pathology, hormonal changes, breast, factors of risk.

An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
P. Srinivasa Rao ◽  
Pradeep Bheemavarapu ◽  
P. S. Latha Kalyampudi ◽  
T. V. Madhusudhana Rao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Chronic Respiratory Disease ◽  
Automated System ◽  
X Rays ◽  
Medical Problems ◽  
X Ray ◽  
Chest X Ray ◽  
Fully Connected

Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. Objective: This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs. Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset. Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Determination of Actinide Elements in Nuclear Fuels by X-Ray Analysis

1974 ◽  
Vol 18 ◽  
pp. 62-75
Author(s):  
A.H.E. von Baeckmann ◽  
D. Ertel ◽  
J. Neuber
Keyword(s):  
Isotope Dilution ◽  
Active Material ◽  
Internal Standard ◽  
Fluorescence Analysis ◽  
Mass Spectrometric ◽  
Operating Conditions ◽  
Automated System ◽  
Isotope Dilution Analysis ◽  
Nuclear Fuels ◽  
X Ray

AbstractX-ray fluorescence analysis has been used for some years at two Institutes of the Karlsruhe Nuclear Research Center to determine routinely thorium, uranium, and plutonium in unirradiated and irradiated nuclear fuels. It has "been used in addition to assay neptunium, americium and curium contained in irradiated samples. The method excels because it is versatile, rapid, tamperproof, and efficient. As a rule, fission products, pollutions, and foreign activities do not interfere with the determination which can be made in a direct way without chemical separation. In practice, the nuclear fuels are dissolved prior to the analysis and a given amount of an appropriate element as an “internal standard” is added. The accuracies attainable (1 σ RSD better than 1 %) are comparable with the mass spectrometric isotope dilution analysis so that in the input analysis of reprocessing plants X-ray fluorescence analysis constitutes a true alternative to the mass spectrometric isotope dilution analysis.Within the efforts to automate the method the prototype of the fully automated system is presently tested with non-active material. A first, not yet fully automated, development stage is being installed in the Karlsruhe Reprocessing Plant. It is anticipated that it will be tested soon under operating conditions. Complete automation of the system is scheduled to be completed at a later date.

Study of the Precision of X-ray Stress Analysis

1976 ◽  
Vol 20 ◽  
pp. 291-307 ◽  
Author(s):  
M. R. James ◽  
J. B. Cohen
Keyword(s):  
Residual Stress ◽  
Total Error ◽  
Computer Control ◽  
Systematic Errors ◽  
Automated System ◽  
Sensitive Detector ◽  
Diffraction Profile ◽  
X Ray ◽  
Counting Strategy ◽  
Position Sensitive

Software is described for complete computer control of residual stress measurements. One program (that incorporates either the two tilt method, the sins| procedure, or the Cohen-Marion technique) has been developed for use with either a normal detector or a position sensitive detector. The operator inputs the desired error in stress and various instrumental parameters that determine systematic errors. The counting strategy to obtain the total error is then determined by the software.Employing this automated system, an investigation of a parabolic fit to the top of a diffraction profile indicates that a three point fit is satisfactory only for sharp profiles.

scholarly journals Breast Evaluation Findings in Calabar, Nigeria

2014 ◽  
Vol 2 (4) ◽  
pp. 650-654
Author(s):  
Anthonia Ikpeme ◽  
Akintunde Akintomide ◽  
Grace Inah ◽  
Afiong Oku
Keyword(s):  
Breast Disease ◽  
Breast Diseases ◽  
Outer Quadrant ◽  
Tertiary Institutions ◽  
X Ray ◽  
The Past ◽  
Significant Difference ◽  
Malignant Breast ◽  
Developed World ◽  
Do So

BACKGROUND:  X-ray and sonomammography constitute a significant option in the early detection and management of breast diseases in the developed world. Unfortunately these modalities became available in Nigeria, only in the past few decades.AIM: The aim of this audit is therefore to document the imaging findings, in the past three years in a developing facility in Nigeria relating them with the demograghic features.METHODS: We prospectively studied the x-ray and sonomammography in all patients, presenting over a three year period, for breast evaluation with the hope of discerning the epidemiologic pattern of breast lesions in this environment.RESULTS: One hundred and forty-five females and four males. Median was 38 years (IQR=30-48). The commonest reason for evaluation was screening. Patients that were below 38 years showed no significant difference in frequency and type of lesion compared with patients over 38 years. The commonest breast pattern was fatty replaced. The upper outer quadrant was the commonest site.CONCLUSION: Patients presenting for breast evaluation in Calabar do so for screening mainly. Patients below 38 are nearly equally affected by malignant breast disease as their older counterparts. The commonest breast pattern was fatty replaced. Digital mammography should be available in all tertiary institutions.

scholarly journals Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report

2020 ◽  
Vol 8 (3) ◽  
pp. 317-326
Author(s):  
Grigory A. Lein ◽  
Natalia S. Nechaeva ◽  
Gulnar М. Mammadova ◽  
Andrey A. Smirnov ◽  
Maxim M. Statsenko
Keyword(s):  
Neural Network ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
X Rays ◽  
X Ray ◽  
Scoliotic Deformity ◽  
Window Method ◽  
Treatment Type

Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type. Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tangent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instrument of automatic analysis of the spine X-rays in scoliosis patients. Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemented and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research. Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving 85% in training neural network to determine the Cobb angle. Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.

scholarly journals Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 9-17
Author(s):  
Hiam Alquran ◽  
Mohammad Alsleti ◽  
Roaa Alsharif ◽  
Isam Abu Qasmieh ◽  
Ali Mohammad Alqudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Features ◽  
Automated System ◽  
Rt Pcr ◽  
False Negatives ◽  
X Ray ◽  
Organ Systems ◽  
Wide Range ◽  
Chest X Ray

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

The value of X-ray contrast methods in the diagnosis of breast diseases

1985 ◽  
Vol 66 (3) ◽  
pp. 232-234
Author(s):  
N. I. Rozhkova
Keyword(s):  
Breast Diseases ◽  
X Ray

Until now, in the diagnosis of breast diseases, mainly clinical and morphological methods have been used.

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