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Статті в журналах з теми "Breast software model"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

George, Baytchev, Inkov Ivan, Kyuchukov Nikola, and Zlateva Emilia. "BREAST CANCER RISK EVALUATION - A CORRELATION BETWEEN MAMMOGRAPHIC DENSITY AND THE GAIL MODEL." International Journal of Surgery and Medicine 1, no. 1 (2015): 18–21. https://doi.org/10.5455/ijsm.20150524105608.

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Background: The Gail model is a statistical tool, which assesses breast cancer probability, based on nonmodifiable risk factors. In contrast, the evaluation of mammographic breast density is an independent and dynamic risk factor influenced by interventions modifying breast cancer risk incidence. Objective: The aim of the present study is to compare the possibilities for risk factor integration and analysis and to search for a correlation between mammographic density and the Gail model for breast cancer risk evaluation. Materials and Methods: The subject of this prospective study is a cohort o

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2

SUN, SHUH-PING, and JING-SHYR CHEN. "THE APPLICATION OF FULL-SCALE 3D ANTHROPOMETRIC DIGITAL MODEL SYSTEM ON BREAST RECONSTRUCTION OF PLASTIC SURGERIES." Biomedical Engineering: Applications, Basis and Communications 15, no. 05 (2003): 200–206. http://dx.doi.org/10.4015/s1016237203000304.

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A full-scale 3D anthropometric digital model system is a set of technology that combined with 3D digital imaging system, computer 3D image processing system, reverse engineering and Computer-Aided Design. The purpose of this studied is to make a full size solid breast model by using the 1:1 anthropometric digital model technique to assist breast reconstruction plastic surgery colon the same size of symmetrical breast of the patient. The full-sized simulating breast model created in this studied not only can assist plastic surgeons by making more symmetric breasts on the other side during the r

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3

Joó, József Gábor, Mónika Csanád, Katalin Tóth, Szabolcs Máté, and Zsolt Nagy. "Risk assessment in familial breast cancer." Orvosi Hetilap 152, no. 19 (2011): 758–62. http://dx.doi.org/10.1556/oh.2011.29110.

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Women with a history of breast cancer are common at centers for cancer genetic risk all over Europe. Given limited health care resources, managing this demand, while achieving good value for money coming from health services, is generally a major challenge. This paper recapitulates and summarizes the available methods of the risk assessment of familial breast cancer. After a systematic review of the literature Gail-model, Claus-model and BOADICEA-model were selected, as well as softwares (LINKAGE software; MENDEL v3.3 software) available in the application of these algorhythms are also summari

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4

Dewi, Siti utami, and Rr Tutik Sri Haryati. "Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review." JIKO (Jurnal Ilmiah Keperawatan Orthopedi) 5, no. 2 (2022): 40–47. http://dx.doi.org/10.46749/jiko.v5i2.70.

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Breast cancer is one of the most common cancer causes of death in sufferers. Breast cancer is the second leading cause of death for Indonesian women. For this reason, a device that can identify the risk of breast cancer is needed. The fuzzy method with the Mamdani model is one method that has been widely used in software development to determine the level of breast cancer risk. Purpose: To know the software development of the Fuzzy Inference System Method with the Mamdani model to identify the level of breast cancer risk. Methods: This research is a literature study with the data collection pr

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5

Casesnoves, Francisco. "RADIOTHERAPY GENETIC ALGORITHM PARETO-MULTIOBJECTIVE OPTIMIZATION OF BIOLOGICAL EFFECTIVE DOSE AND CLONOGENS MODELS FOR BREAST TUMOR IMPROVED TREATMENT." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3102–14. http://dx.doi.org/10.47191/ijmcr/v11i1.02.

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BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective Genetic Algorithms (GA) software. Secondly, the NEffective (Effective Tumor Population Clonogens Number) model optimization for breast cancer clonogens parameters determination in TPO (Treatment Planning Optimization) is carried out with 3D Graphical and Interior Optimization methods. BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective GA software. Results comprise imaging process series and numerical values of NEffective model for brea

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6

Al Husaini, Mohammed Abdulla Salim, Mohamed Hadi Habaebi, F. M. Suliman, Md Rafiqul Islam, Elfatih A. A. Elsheikh, and Naser A. Muhaisen. "Influence of Tissue Thermophysical Characteristics and Situ-Cooling on the Detection of Breast Cancer." Applied Sciences 13, no. 15 (2023): 8752. http://dx.doi.org/10.3390/app13158752.

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This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. Th

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7

Al-Fahaidy, Farouk A. K., Belal Al-Fuhaidi, Ishaq AL-Darouby, Faheem AL-Abady, Mohammed AL-Qadry, and Abdurhman AL-Gamal. "A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques." Applied Computational Intelligence and Soft Computing 2022 (September 20, 2022): 1–17. http://dx.doi.org/10.1155/2022/3895976.

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Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors b

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8

Zoramawa, A. B,, U. Usman, B. A. Magaji, and M. Rabiu. "STUDY ON THE PERFORMANCE OF SOME PARAMETRIC PROPORTIONAL HAZARD MODELS AND SEMI-PARAMETRIC MODEL IN THE ANALYSIS OF BREAST CANCER DATA." FUDMA JOURNAL OF SCIENCES 7, no. 5 (2023): 181–84. http://dx.doi.org/10.33003/fjs-2023-0705-2011.

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A study is conducted on medical records of 416 breast cancer patients. Analysis was performed using the R software version R3.6.3, and the level of significance was set at 0.05. The work employed three models which were based on Exponential, Weibull and Cox Regression models. The Weibull proportional model (AIC=1959.038) was the most appropriate model among the considered models, based on the Akaike information criterion (AIC). Results of the best fitted model showed that the survival time of breast cancer patients is significantly affected by age, age at diagnosis, and treatment taken at 95%.

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9

Francisco, Casesnoves. "RADIOTHERAPY GENETIC ALGORITHM PARETO-MULTIOBJECTIVE OPTIMIZATION OF BIOLOGICAL EFFECTIVE DOSE AND CLONOGENS MODELS FOR BREAST TUMOR IMPROVED TREATMENT." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3102–12. https://doi.org/10.5281/zenodo.7520333.

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Анотація:

BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective Genetic Algorithms (GA) software. Secondly, the N<sub>Effective</sub> (Effective Tumor Population Clonogens Number) model optimization for breast cancer clonogens parameters determination in TPO (Treatment Planning Optimization) is carried out with 3D Graphical and Interior Optimization methods. BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective GA software. Results comprise imaging process series and numerical values of N<sub>Eff

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10

Baneva, Yanka, Kristina Bliznakova, Lesley Cockmartin, et al. "Evaluation of a breast software model for 2D and 3D X-ray imaging studies of the breast." Physica Medica 41 (September 2017): 78–86. http://dx.doi.org/10.1016/j.ejmp.2017.04.024.

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11

Mazzola, Emanuele, Amanda Blackford, Giovanni Parmigiani, and Swati Biswas. "Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO." Cancer Informatics 14s2 (January 2015): CIN.S17292. http://dx.doi.org/10.4137/cin.s17292.

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BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee's family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. I

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12

Muayad, Sadik Croock, Dhyaa Khuder Saja, Esho Korial Ayad, and Salman Mahmood Sahar. "Early detection of breast cancer using mammography images and software engineering process." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1784–94. https://doi.org/10.12928/TELKOMNIKA.v18i4.14718.

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The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the p

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13

Baneva, Yanka, Lesley Cockmartin, Hilde Bosmans, et al. "Evaluation of breast software model for X-ray 2D and 3D mammography imaging." Physica Medica 32 (September 2016): 255–56. http://dx.doi.org/10.1016/j.ejmp.2016.07.548.

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14

Alsayadi, Hamzah A., Abdelaziz A. Abdelhamid, El-Sayed M. El El-Kenawy, Abdelhameed Ibrahim, and Marwa M. Eid. "Ensemble of Machine Learning Fusion Models for Breast Cancer Detection Based on the Regression Model." Fusion: Practice and Applications 9, no. 2 (2022): 19–26. http://dx.doi.org/10.54216/fpa.090202.

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Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based o

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15

Rispoli, Joseph V., Steven M. Wright, Craig R. Malloy, and Mary P. McDougall. "Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations." Journal of Biomedical Graphics and Computing 7, no. 1 (2017): 1. http://dx.doi.org/10.5430/jbgc.v7n1p1.

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Background: Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, el

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16

Thottathyl, Hymavathi, Kanadam Karteeka Pavan, and Rajeev Priyatam Panchadula. "Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm." Revue d'Intelligence Artificielle 34, no. 6 (2020): 763–69. http://dx.doi.org/10.18280/ria.340610.

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Breast cancer is one of the world's most advanced and most common cancers occurring in women. An early diagnosis of breast cancer offers treatment for it; therefore, several experiments are in development establishing approaches for the early detection of breast cancer. The great increase in research in the last decade in microarray data processing is a potent tool of diagnosing diseases. Based on genomic knowledge, micro-arrays have changed the way clinical pathology recognizes, identifies, and classifies the diseases of humans, particularly those of cancer. In this article, we examined micro

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17

Lu, Yuanyuan, Yunqing Chen, Cheng Chen, Junlai Li, Kunlun He, and Ruoxiu Xiao. "An Intelligent Breast Ultrasound System for Diagnosis and 3D Visualization." Electronics 11, no. 14 (2022): 2116. http://dx.doi.org/10.3390/electronics11142116.

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Background: Ultrasonography is the main examination method for breast diseases. Ultrasound imaging is currently relied upon by doctors to form statements of characteristics and locations of lesions, which severely limits the completeness and effectiveness of ultrasound image information. Moreover, analyzing ultrasonography requires experienced ultrasound doctors, which are not common in hospitals. Thus, this work proposes a 3D-based breast ultrasound system, which can automatically diagnose ultrasound images of the breasts and generate a representative 3D breast lesion model through typical ul

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18

Rezaeipanah, Amin, Rahmad Syah, Siswi Wulandari, and A. Arbansyah. "Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis." Inteligencia Artificial 24, no. 67 (2021): 147–56. http://dx.doi.org/10.4114/intartif.vol24iss67pp147-156.

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Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neur

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19

Darvishpour, Azar, Soheila Mazloum Vajari, and Sara Noroozi. "Can Health Belief Model Predict Breast Cancer Screening Behaviors?" Open Access Macedonian Journal of Medical Sciences 6, no. 5 (2018): 949–53. http://dx.doi.org/10.3889/oamjms.2018.183.

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BACKGROUND: Breast cancer is the second cause of cancer-related death among women. Prevention programs insist on the early diagnosis and screening to reduce the mortality rate.AIM: The study was conducted to determine the predictors of breast cancer screening behaviours based on the health belief model.MATERIAL AND METHODS: The present cross-sectional study was conducted by involving 304 women ranging from 20 to 65 years of age, living in East Guilan cities, the North of Iran, in 2015 using two-stage cluster sampling. The research instrument was Champion's Health Belief Model Scale. The data w

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20

Liu, Shuang, Min Tang, Shuqin Ruan, Feng Wei, and Jiaxi Lu. "Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms." Scientific Programming 2021 (November 3, 2021): 1–8. http://dx.doi.org/10.1155/2021/5289128.

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This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity,

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21

Al-Jobawi, Zainab J., Nawar Y. Al-Saegh, Assel W. Khudair, et al. "Breast Density Classified Using AI-aided Mammographic Breast Density Classification Tool and Validated among Hilla Women's Sample." EJSMT 1, no. 2 (2025): 33–41. https://doi.org/10.59324/ejsmt.2025.1(2).02.

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Background: Breast density evaluation is very important in breast cancer screening, since it affects both cancer risk and the sensitivity of mammography detection. Radiologists' conventional visual judgements could differ greatly among readers. Automated software driven by machine learning has been created to examine mammographic pictures and generate consistent breast density classifications in order to solve this. This work intends to test the degree of agreement between conventional visual interpretation by radiologists and artificial intelligence (AI)-based computerised breast density eval

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22

Croock, Muayad S., Ayad E. Korial, Tara F. Kareem, Qusay Sh Hamad, and Ghaidaa M. Abdulsaheb. "Software Engineering Model Based Early Detection Method of Breast Cancer using Deep-Learning Framework." Journal of Engineering and Applied Sciences 14, no. 16 (2019): 5775–81. http://dx.doi.org/10.36478/jeasci.2019.5775.5781.

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23

Sindi, Rooa, Yin How Wong, Chai Hong Yeong, and Zhonghua Sun. "Quantitative Measurement of Breast Density Using Personalized 3D-Printed Breast Model for Magnetic Resonance Imaging." Diagnostics 10, no. 10 (2020): 793. http://dx.doi.org/10.3390/diagnostics10100793.

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Despite the development and implementation of several MRI techniques for breast density assessments, there is no consensus on the optimal protocol in this regard. This study aimed to determine the most appropriate MRI protocols for the quantitative assessment of breast density using a personalized 3D-printed breast model. The breast model was developed using silicone and peanut oils to simulate the MRI related-characteristics of fibroglandular and adipose breast tissues, and then scanned on a 3T MRI system using non-fat-suppressed and fat-suppressed sequences. Breast volume, fibroglandular tis

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24

Akkur, Erkan, Fuat Türk, and Osman Erogul. "Breast cancer classification using a novel hybrid feature selection approach." Neural Network World 33, no. 2 (2023): 67–83. http://dx.doi.org/10.14311/nnw.2023.33.005.

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Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SV

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25

Gennaro, Gisella, Melissa L. Hill, Elisabetta Bezzon, and Francesca Caumo. "Quantitative Breast Density in Contrast-Enhanced Mammography." Journal of Clinical Medicine 10, no. 15 (2021): 3309. http://dx.doi.org/10.3390/jcm10153309.

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Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the p

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26

Rahman, Abdullah Sani Abd, Suraya Masrom, Rahayu Abdul Rahman, and Roslina Ibrahim. "Rapid Software Framework for the Implementation of Machine Learning Classification Models." International Journal of Emerging Technology and Advanced Engineering 11, no. 8 (2021): 8–18. http://dx.doi.org/10.46338/ijetae0821_02.

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Reseachers have acknowledged that machine learning is useful to be utilized in many different domains of complex real life problem. However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. This paper provides an insight of a rapid software framework for implementing machine learning. This paper also demonstrates the empirical research results of machine learning classification mo

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27

Oblak, Tjasa, Vesna Zadnik, Mateja Krajc, Katarina Lokar, and Janez Zgajnar. "Breast cancer risk based on adapted IBIS prediction model in Slovenian women aged 40–49 years - could it be better?" Radiology and Oncology 54, no. 3 (2020): 335–40. http://dx.doi.org/10.2478/raon-2020-0040.

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AbstractBackgroundThe aim of the study was to assess the proportion of women that would be classified as at above-average risk of breast cancer based on the 10 year-risk prediction of the Slovenian breast cancer incidence rate (S-IBIS) program in two presumably above-average breast cancer risk populations in age group 40-49 years: (i) women referred for any reason to diagnostic breast centres and (ii) women who were diagnosed with breast cancer aged 40–49 years. Breast cancer is the commonest female cancer in Slovenia, with an incidence rate below European average. The Tyrer-Cuzick breast canc

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28

Chen, Chien-Hsing. "A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection." Applied Soft Computing 20 (July 2014): 4–14. http://dx.doi.org/10.1016/j.asoc.2013.10.024.

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Aisyah, Siti, Aditya Prayugo Hariyanto, Endarko Endarko, et al. "Evaluation Treatment Planning for Breast Cancer Based on Dose-Response Model." Jurnal ILMU DASAR 22, no. 1 (2021): 75. http://dx.doi.org/10.19184/jid.v22i1.19732.

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The delivery of radiation therapy to patients requires prior planning made by medical physicists to achieve radiotherapy goals. Radiotherapy has a plan to eradicate the growth of cancer cells by giving high doses and minimizing the radiation dose to normal tissue. Evaluation of planning is generally done based on dosimetric parameters, such as minimum dose, maximum dose, and means dose obtained from the DVHs data. Based on the same DVHs, data were evaluate dinterms of biological effects to determine the highest possible toxicity in normal tissue after the tumor had been treated with radiation

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30

Ovaku, Momohjimoh Idris, Stephen Eyije Abech, Gideon Adamu Shallangwa, and Adamu Uzairu. "IN SILICO ELUCIDATION OF SOME QUINOLINE DERIVATIVES WITH POTENT ANTI-BREAST CANCER ACTIVITIES." Journal of Engineering and Exact Sciences 6, no. 1 (2020): 0008–14. http://dx.doi.org/10.18540/jcecvl6iss1pp0008-0014.

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Abstract: The toxicity and high resistance to the commercially sold breast-cancer drugs have become more alarming and the demand to produce new and less toxic breast-cancer drugs arises. In silico studies was carried out on some quinoline derivatives to investigate their reported activities against breast cancer and thereby generate a model with a better activity against breast cancer. The chemical structures of the compounds were optimized using Spartan software at Density Functional Theory (DFT) level, utilizing the B3LYP/ 6-31G* basis set. Four QSAR models were generated using Multi-Linear

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31

Kim, Jaeyoon, Minhyeok Lee, and Junhee Seok. "Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data." Machine Learning: Science and Technology 4, no. 2 (2023): 025026. http://dx.doi.org/10.1088/2632-2153/acd987.

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Abstract Breast cancer has the highest incidence and death rate among women; moreover, its metastasis to other organs increases the mortality rate. Since several studies have reported gene expression and cancer prognosis to be related, the study of breast cancer metastasis using gene expression is crucial. To this end, a novel deep neural network architecture, deep learning-based cancer metastasis estimator (DeepCME), is proposed in this paper for predicting breast cancer metastasis. However, the problem of overfitting occurs frequently while training deep learning models using gene expression

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32

Bliznakova, Kristina, Zhivko Bliznakov, and Nikolay Dukov. "Computational simulations and assessment of two approaches for x-ray phase contrast imaging." Journal of Physics: Conference Series 2162, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2162/1/012013.

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Abstract X-ray phase-contrast imaging is a high-resolution imaging that permits an increase of the perceptibility of the details in three-dimensional objects, such as human tissues compared to conventional absorption imaging. There are different approaches for implementing phase-contrast imaging and their introduction into clinical practice requires advanced computational tools. A long-term goal of our research is the development of computational models of breast phase-contrast imaging. The aim of this study is to develop a software module for implementing grating-based phase-contrast imaging.

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Madhu, B., N. C. Ashok, and S. Balasubramanian. "Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 193–96. http://dx.doi.org/10.5194/isprsarchives-xl-8-193-2014.

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Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007&ndash;2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the

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Huang, Yihong, Shuo Zheng, Yu Lin, and Haiyan Miao. "Breast Cancer Classification Prediction Based on Ultrasonic Image Feature Recognition." Journal of Healthcare Engineering 2021 (September 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/4025597.

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Exploring an effective method to manage the complex breast cancer clinical information and selecting a suitable classifier for predictive modeling still require continuous research and verification in the actual clinical environment. This paper combines the ultrasound image feature algorithm to construct a breast cancer classification model. Furthermore, it combines the motion process of the ultrasound probe to accurately connect the ultrasound probe to the breast tumor. Moreover, this paper constructs a hardware and software system structure through machine vision algorithms and intelligent m

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Bandaru, Satish Babu, Natarajasivan Deivarajan, and Rama Mohan Babu Gatram. "Investigations on Deep Learning Techniques for Analysing Mammograms." Revue d'Intelligence Artificielle 36, no. 3 (2022): 451–57. http://dx.doi.org/10.18280/ria.360313.

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Mammograms have been acknowledged as one of the most reliable screening tools as well as a key diagnostic mechanism for early breast cancer detection. Though mammography is a valuable screening tool for detecting malignant growth in breasts, its competence as a diagnostic tool is heavily reliant on the radiologists’ understanding. Automated systems are now widely used for detection of breast cancer. Image processing techniques were widely used in automated systems for classifying mammograms. Of late with the advent of deep learning (DL) where images can be processed directly for classification

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Tian, Yuan, Jingnan Wang, Qing Wen, et al. "The Significance of Tumor Microenvironment Score for Breast Cancer Patients." BioMed Research International 2022 (April 28, 2022): 1–27. http://dx.doi.org/10.1155/2022/5673810.

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Purpose. This study was designed to clarify the prognostic value of tumor microenvironment score and abnormal genomic alterations in TME for breast cancer patients. Method. The TCGA-BRCA data were downloaded from TCGA and analyzed with R software. The results from analyses were further validated using the dataset from GSE96058, GSE124647, and GSE25066. Results. After analyzing the TCGA data and verifying it with the GEO data, we developed a TMEscore model based on the TME infiltration pattern and validated it in 3273 breast cancer patients. The results suggested that our TMEscore model has hig

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Usman, Ausanti, Rasipin Rasipin, and Sutopo Patriajati. "Mobile Information Breastfeeding Model (MIB-Model) On Behaviors and Self-Efficacy of Breastfeeding Among Mothers." International Journal of Nursing and Health Services (IJNHS) 3, no. 5 (2020): 549–59. http://dx.doi.org/10.35654/ijnhs.v3i5.258.

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Breastfeeding is proven to have long-term health benefits for both mothers and infants. The advancement of mobile technology is very useful in promoting health that can change health behaviors. The success of breast milk is not separated from the methods and media used. The study aimed to develop MIB-Model and to examine the effect of MIB-Model on behavior and self-efficacy of breastfeeding among mothers in providing breast milk. The application development method with the software development Live cycle (SDLC) with the waterfall model. The test model is conducted with Quasi-experiment with pr

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Hengpraprohm, Supoj, and Suwimol Jungjit. "Ensemble Feature Selection for Breast Cancer Classification using Microarray Data." Inteligencia Artificial 23, no. 65 (2020): 100–114. http://dx.doi.org/10.4114/intartif.vol23iss65pp100-114.

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For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed ‘EnSNR’ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelev

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Orlov, A. E., O. I. Kaganov, V. N. Saveliev, M. V. Tkachev, A. P. Borisov, and P. L. Kruglova. "A Mathematical Model for Complete Morphological Regression in Primary Operable HER2-Positive Breast Cancer." Creative surgery and oncology 11, no. 1 (2021): 5–9. http://dx.doi.org/10.24060/2076-3093-2021-11-1-5-9.

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Background. Breast cancer (BC) is distinguished with its biological tumour subtypes as luminal A, B, HER2-positive and triple-negative. The current clinical guidelines of the Russian Ministry of Health prescribe neoadjuvant targeted chemotherapy as combined treatment in the HER2-positive cancer subtype. An adequate model for treatment efficacy prediction in such patients had been missing to date.Aim. Development of a mathematical model and its computer realisation for complete morphological regression estimation in patients with primary operable HER2-positive breast cancer.Materials and method

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Ghosh, Sarada, Guruprasad Samanta, and Manuel De la Sen. "Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy." Computation 9, no. 5 (2021): 59. http://dx.doi.org/10.3390/computation9050059.

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Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles metho

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Rodrigues, Jackson, Ashwini Amin, Chandavalli Ramappa Raghushaker, et al. "Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo." Laboratory Investigation 101, no. 7 (2021): 952–65. http://dx.doi.org/10.1038/s41374-021-00597-3.

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AbstractIn the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the correspon

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T. Chandrasekaran, Sanjeev, Ruobing Hua, Imon Banerjee, and Arindam Sanyal. "A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification." Electronics 9, no. 3 (2020): 515. http://dx.doi.org/10.3390/electronics9030515.

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We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype co

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Khomsi, Zakaryae, Mohamed El Fezazi, Achraf Elouerghi, and Larbi Bellarbi. "EVALUATING THE FEASIBILITY OF THERMOGRAPHIC IMAGES FOR PREDICTING BREAST TUMOR STAGE USING DCNN." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 1 (2024): 99–104. http://dx.doi.org/10.35784/iapgos.5555.

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Early-stage and advanced breast cancer represent distinct disease processes. Thus, identifying the stage of tumor is a crucial procedure for optimizing treatment efficiency. Breast thermography has demonstrated significant advancements in non-invasive tumor detection. However, the accurate determination of tumor stage based on temperature distribution represents a challenging task, primarily due to the scarcity of thermal images labeled with the stage of tumor. This work proposes a transfer learning approach based on Deep Convolutional Neural Network (DCNN) with thermal images for predicting b

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Clarke, Nathan, Andrew Dettrick, and Jane Armes. "Efficacy of training a deep learning model for mitotic count in breast carcinoma using opensource software." Pathology 53 (July 2021): S23—S24. http://dx.doi.org/10.1016/j.pathol.2021.06.019.

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Budak, Ümit, Zafer Cömert, Zryan Najat Rashid, Abdulkadir Şengür, and Musa Çıbuk. "Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images." Applied Soft Computing 85 (December 2019): 105765. http://dx.doi.org/10.1016/j.asoc.2019.105765.

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J, Kamalakannan, and Chandana Mani R K. "ERNet : Enhanced ResNet for classification of breast histopathological images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 22, no. 2 (2024): 53–68. http://dx.doi.org/10.5565/rev/elcvia.1614.

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Inspite of expeditious approaches in field of breast cancer, histopathological analysis is considered as gold standard in diagnosis of cancer. Researchers are working tremendously to automate the detection and analysis of breast histology images, which confess in improving the accuracy and also induce the mimisation of processing time. Deep learning models are providing greater contribution in solving several image classification tasks. In this paper we propose a model to classify breast histological images, which is redesigned from existing ResNet architecture that minimises model parameters

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Yu, Tianshui, Weilun Cheng, Ting Wang, et al. "Survival Outcomes of Breast-Conserving Therapy versus Mastectomy in Early-Stage Breast Cancer, Including Centrally Located Breast Cancer: A SEER-Based Study." Breast Journal 2022 (August 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/5325556.

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Purpose. This study aims to analyze the survival outcomes of breast cancer (BC) patients, especially centrally located breast cancer (CLBC) patients undergoing breast-conserving therapy (BCT) or mastectomy. Methods. Surveillance, epidemiology, and end results (SEER) data of patients with T1-T2 invasive ductal or lobular breast cancer receiving BCT or mastectomy were reviewed. We used X-tile software to convert continuous variables to categorical variables. Chi-square tests were utilized to compare baseline information. The multivariate logistic regression model was performed to evaluate the re

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Новикова, Е. И., Е. Н. Коровин, and Н. М. Агарков. "DEVELOPMENT OF A COMPUTER SIMULATION MODEL FOR DIAGNOSTICS OF BREAST DISEASES." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ 23, no. 2 (2024): 163–67. http://dx.doi.org/10.36622/1682-6523.2024.23.2.022.

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Статья посвящена разработке модели рационального принятия решения для диагностики больных с заболеваниями молочной железы на основе сетей Петри. В настоящее время проблема диагностики заболеваний молочной железы (МЖ) является очень актуальной. Связано это с тем, что, несмотря на проведение многих мониторинговых предприятий, статистика упорно показывает рост заболеваний МЖ у женщин в разных возрастных группах. Основные 2 вида заболеваний: доброкачественные и злокачественные. Среди злокачественных самый опасный для жизни женщин – это рак молочной железы (РМЖ), занимает первое место по летальност

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Phillips, Kelly-Anne, Emma J. Steel, Ian Collins, et al. "Transitioning to routine breast cancer risk assessment and management in primary care: what can we learn from cardiovascular disease?" Australian Journal of Primary Health 22, no. 3 (2016): 255. http://dx.doi.org/10.1071/py14156.

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To capitalise on advances in breast cancer prevention, all women would need to have their breast cancer risk formally assessed. With ~85% of Australians attending primary care clinics at least once a year, primary care is an opportune location for formal breast cancer risk assessment and management. This study assessed the current practice and needs of primary care clinicians regarding assessment and management of breast cancer risk. Two facilitated focus group discussions were held with 17 primary care clinicians (12 GPs and 5 practice nurses (PNs)) as part of a larger needs assessment. Prima

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Rajasekaran Subramanian, Et al. "Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 61–65. http://dx.doi.org/10.17762/ijritcc.v11i10.8465.

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Breast cancer is the second largest cancer caused in the world due to the uncontrollable growth in breast cells. Nottingham Grading is the internationally acceptable system to grade breast cancer. Nuclear pleomorphism is one of the breast cancer biomarkers for computing Nottingham grading. Pathologists grade nuclear pleomorphism on breast cancer glass tissue slides using a conventional microscope which is time consuming and has considerable inter-observer variability between pathologists. The paper proposed an Artificial Intelligence (AI) deep learning model to grade grade1, grade2, grade3 nuc

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