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Jammu Kashmir, India, Asia, Algorithms, Cancer, Cervical Cancer, Diagnostics and Screening, Health and Medicine, Oncology, Women's Health Keywords: Jammu Kashmir; India; Asia; Algorithms; Cancer; Cervical Cancer; Diagnostics and Screening; Health and Medicine; Oncology; Women's Health EN Jammu Kashmir India Asia Algorithms Cancer Cervical Cancer Diagnostics and Screening Health and Medicine Oncology Women's Health 1014 1014 1 10/03/23 20231006 NES 231006 2023 OCT 3 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Cervical Cancer is now available. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

A recent study conducted at Shiraz University aimed to improve the accuracy of breast cancer diagnosis by using a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods. The researchers tested their algorithm on three common breast cancer datasets and found that it significantly improved accuracy while reducing the number of features needed for diagnosis. The study suggests that the proposed algorithm has the potential to enhance breast cancer diagnosis and reduce cost and time. Further information on the study can be found in the International Journal of Computational Intelligence Systems. [Extracted from the article]

A recent report from Lanzhou University in China discusses the use of a new algorithm called Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm (HLCCOA) to improve the accuracy of extreme learning machine diagnostics in breast cancer. The HLCCOA algorithm addresses the limitations of traditional crayfish optimization algorithms by utilizing chaotic sequences for population position initialization and implementing a hierarchical learning mechanism. Rigorous testing shows that the HLCCOA-optimized extreme learning machine model outperforms other benchmark models in terms of accuracy, sensitivity, and specificity for breast cancer diagnosis. This research highlights the potential practicality and robustness of the HLCCOA algorithm in improving breast cancer diagnostics. [Extracted from the article]

A recent study conducted by researchers at Wenzhou University in China focuses on improving the accuracy of image segmentation for breast cancer diagnosis. The study proposes a refined whale optimization algorithm that combines a quantum phase interference mechanism and an enhanced solution quality strategy. The algorithm is compared to other state-of-the-art algorithms and is tested on benchmark datasets to validate its performance. The results demonstrate the effectiveness of the segmentation method in both low and high threshold levels, making it a valuable technique for practical applications. The study has been peer-reviewed and provides potential avenues for further research in breast cancer diagnosis. [Extracted from the article]

A recent study conducted by researchers at Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia, has developed a new approach called Swin-GA-RF to enhance the classification of cervical cells in Pap smear images for the detection of cervical cancer. The Swin-GA-RF approach combines the Swin transformer, genetic algorithm feature selection, and a random forest classifier. The researchers compared the performance of Swin-GA-RF with pre-trained CNN models and found that Swin-GA-RF outperformed other models in both binary and five-class classification tasks. These findings suggest that the Swin-GA-RF approach could be a valuable tool for early diagnosis and screening programs for cervical cancer. [Extracted from the article]

Researchers at the University of Mustapha Stambouli in Algeria have developed a new method for accurately classifying breast cancer using machine learning techniques. The method, called Correlation technique-Modified Grey Wolf Optimizer (CMGWO), involves two stages: feature selection and classification. The researchers used the Wisconsin Diagnostic Breast Cancer (WDBC) database to test the performance of their method and found that it significantly increased the accuracy rate of classification. The study concludes that CMGWO is a reliable approach for detecting breast cancer, with an accuracy rate of 99.12%. [Extracted from the article]

A recent report from Universitas Multimedia Nusantara highlights the global concern of breast cancer, which is the leading cancer type in terms of new cases and among the top five cancer types with the most deaths. The research aims to improve the identification of breast cancer comorbidities and ultimately reduce the number of deaths from this condition. The study utilizes network analysis and community detection algorithms to identify comorbidities associated with breast cancer. The findings reveal several diseases that are commonly associated with breast cancer, providing valuable insights for medical professionals. [Extracted from the article]

A recent study conducted at Kanazawa University in Japan explores the use of machine learning algorithms for the diagnosis of cervical cancer. The researchers propose a novel feature selection strategy called the Binary Harris Hawks Optimization (BHHO) algorithm, which aims to reduce the number of features used in diagnosis while improving accuracy. The BHHO algorithm demonstrates better stability and accuracy compared to other common wrapper-based feature selection methods, not only on cervical cancer datasets but also on other disease datasets. This research contributes to the ongoing efforts to improve early diagnosis and treatment of cervical cancer, which is one of the leading causes of cancer-related deaths among females worldwide. [Extracted from the article]

A recent study conducted by researchers at the University of Granada in Spain has developed a new app called PaiNEd that aims to assess pain experience in survivors of breast cancer. The app includes an algorithm that determines whether patients are experiencing pain predominantly related to central sensitization (CS) symptoms. The study found that the app demonstrated reliability in evaluating pain experience and could be a valuable tool for healthcare professionals involved in pain education programs. This research provides a potential solution for assessing and managing pain in breast cancer survivors. [Extracted from the article]

Keywords: Chennai; India; Asia; Algorithms; Breast Cancer; Breast Cancer Screening; Cancer; Diagnostics and Screening; Health and Medicine; Mammogram; Mammography; Oncology; Risk and Prevention; Women's Health EN Chennai India Asia Algorithms Breast Cancer Breast Cancer Screening Cancer Diagnostics and Screening Health and Medicine Mammogram Mammography Oncology Risk and Prevention Women's Health 234 234 1 06/26/23 20230627 NES 230627 2023 JUN 27 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators discuss new findings in Oncology - Breast Cancer. For more information on this research see: Cancer Regions In Mammogram Images Using Anfis Classifier Based Probability Histogram Segmentation Algorithm. [Extracted from the article]

Keywords: Wenzhou; People's Republic of China; Asia; Algorithms; Breast Cancer; Cancer; Health and Medicine; Metaheuristic Algorithm; Oncology; Women's Health EN Wenzhou People's Republic of China Asia Algorithms Breast Cancer Cancer Health and Medicine Metaheuristic Algorithm Oncology Women's Health 1381 1381 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Researchers detail new data in Oncology - Breast Cancer. Keywords for this news article include: Wenzhou, People's Republic of China, Asia, Algorithms, Breast Cancer, Cancer, Health and Medicine, Metaheuristic Algorithm, Oncology, Women's Health, Wenzhou University. First, the original algorithm comparison experiment on IEEE CEC2014 includes strategy comparison, dimension comparison, exploration/exploitation balance, and population diversity experiments. [Extracted from the article]

Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Keywords: Algorithms; Breast Cancer; Breast Cancer Screening; Cancer; Cancer Detection; Diagnostics and Screening; Fuzzy Logic; Health and Medicine; Mammogram; Mammography; Oncology; Risk and Prevention; Women's Health EN Algorithms Breast Cancer Breast Cancer Screening Cancer Cancer Detection Diagnostics and Screening Fuzzy Logic Health and Medicine Mammogram Mammography Oncology Risk and Prevention Women's Health 968 968 1 07/24/23 20230728 NES 230728 2023 JUL 25 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- New research on cancer detection is the subject of a new report. [Extracted from the article]

Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. Keywords: Algorithms; Breast Cancer; Cancer; Cyborgs; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Women's Health EN Algorithms Breast Cancer Cancer Cyborgs Diagnostics and Screening Emerging Technologies Health and Medicine Machine Learning Oncology Women's Health 471 471 1 03/23/23 20230324 NES 230324 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Current study results on breast cancer have been published. [Extracted from the article]

A recent report from investigators at the School of Computing in Tamil Nadu, India, discusses the use of swarm intelligent metaheuristic optimization algorithms-based artificial neural network models for breast cancer diagnosis. The report emphasizes the importance of early detection and the use of optimization algorithms to improve accuracy and efficiency in predicting breast cancer. The researchers compare different approaches and highlight the significance of swarm intelligent algorithms-based optimized artificial neural network models. The report also discusses evaluation metrics and future research directions in this field. This research has been peer-reviewed and provides valuable insights into breast cancer diagnosis. [Extracted from the article]

Researchers from the University of Malaysia Perlis have developed a new method for detecting and segmenting the nucleus cell region in Pap smear images for cervical cancer screening. The proposed method, which includes color adjustment, k-means, and a modified Bradley algorithm, has been found to significantly and efficiently segment the nucleus cell region compared to the original Bradley algorithm. The researchers achieved high accuracy, sensitivity, and F-measure results, indicating the effectiveness of their approach. This research provides a new perspective on improving the convenience and accuracy of cervical cancer screening. [Extracted from the article]

Researchers from Regeneron Pharmaceuticals Inc. conducted a study on the application of multiple validated algorithms for identifying incident breast cancer among individuals with atopic dermatitis (AD). They tested two algorithms in multiple insurance claims databases and found that Algorithm 2 provided similar estimates to those of the Surveillance, Epidemiology, and End Results (SEER) program. However, they noted that the replicability of these algorithms in different data sources or subpopulations is rarely tested, and caution should be exercised when using them to calculate incidence or prevalence estimates. The study was supported by Sanofi and published in Pharmacoepidemiology and Drug Safety. [Extracted from the article]

A recent study conducted by researchers at Shanghai University of Electric Power explores the use of privacy-preserving algorithms in breast cancer prediction. The researchers highlight the need for pre-diagnosis testing to alleviate the strain on healthcare resources. They propose a highly optimized two-party logistic regression algorithm based on the CKKS scheme, which aims to enhance the accuracy and efficiency of pre-diagnosis testing while upholding data privacy. Simulation experiments demonstrate that the model's prediction accuracy exceeds 96% for two-sided encrypted data. This research contributes to the field of machine learning in healthcare and offers potential solutions for collaborative training of models while protecting private data. [Extracted from the article]

Researchers at Shaqra University in Saudi Arabia have developed a new method for detecting and quantifying Breast Arterial Calcification (BAC) in mammograms. The method combines Enhanced Wolf Pack Algorithm (EWPA) for feature selection and Dense-kUNet segmentation for image classification. The proposed technique achieved high precision, recall, F-measure, and accuracy when compared to other methods on the Digital Database for Screening Mammography (DDSM). This research provides a promising approach to improve breast cancer screening and diagnosis. [Extracted from the article]

A new research study from the Autonomous University in Morelos, Mexico, has developed an improved object detection algorithm for breast cancer screening. The algorithm combines the Khalil conformable derivative and the Hessian matrix to enhance image quality and accuracy in identifying breast cancer. The research found that the algorithm provides better visual identification of conditions such as breast cancer, calcifications, and cerebral arteriovenous malformations, improving the accuracy of clinical diagnosis and subsequent monitoring. The study has been peer-reviewed and offers potential applications in medical image processing and analysis. [Extracted from the article]

Researchers at Lovely Professional University in Phagwara, India have developed a hybrid feature selection technique to improve the speed and performance of machine learning models in diagnosing breast cancer. The technique combines Regularized Neighborhood Component Analysis (RNCA) and Binary Teaching Learning Based Optimization (BTLBO) algorithms to select the best features for classification. The results of the study showed that the hybrid model outperformed other feature selection methods in terms of classification accuracy, recall, and AUC measures. This research has been peer-reviewed and published in The International Arab Journal of Information Technology. [Extracted from the article]

This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. Keywords: Algorithms; Breast Cancer; Cancer; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Networks; Neural Networks; Oncology; Women's Health EN Algorithms Breast Cancer Cancer Cyborgs Emerging Technologies Health and Medicine Machine Learning Networks Neural Networks Oncology Women's Health 2023 FEB 21 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- New research on breast cancer is the subject of a new report. [Extracted from the article]

Algorithms, Breast Cancer, Cancer, Cancer Risk, Health and Medicine, Machine Learning, Oncology, Risk and Prevention, Support Vector Machines, Women's Health Keywords: Algorithms; Breast Cancer; Cancer; Cancer Risk; Health and Medicine; Machine Learning; Oncology; Risk and Prevention; Support Vector Machines; Women's Health EN Algorithms Breast Cancer Cancer Cancer Risk Health and Medicine Machine Learning Oncology Risk and Prevention Support Vector Machines Women's Health 108 108 1 07/24/23 20230728 NES 230728 2023 JUL 25 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Current study results on breast cancer have been published. [Extracted from the article]

A study conducted by the ACECR Institute of Higher Education in Isfahan, Iran, aimed to improve the performance of the Computer-Aided Detection (CAD) system for breast cancer detection using thermal images. The researchers developed an intelligent method that involved feature extraction, feature selection, classification, and artificial intelligence techniques. The method was evaluated using breast thermograms from 200 subjects, and the results showed high accuracy, sensitivity, and specificity when using different classifier algorithms. This study demonstrates the effectiveness of the intelligent method in improving the accuracy of breast cancer detection. [Extracted from the article]

A consortium led by scientists from the German Cancer Research Center (DKFZ) and the National Center for Tumor Diseases (NCT) has developed an online tool called "Metrics Reloaded" to help users select the most suitable algorithm for AI-supported image analysis. The tool provides information on the strengths, weaknesses, and limitations of different validation metrics used to evaluate the performance of algorithms in medical image analysis. The researchers aim to improve the quality and reliability of AI-supported image analysis results, promoting confidence in routine clinical practice. The tool is applicable to various categories of image analysis problems and can be used for different types of medical images as well as other areas of image analysis. [Extracted from the article]

A research study conducted at GLA University in Mathura, India, explores the use of metaheuristic feature selection algorithms to improve the accuracy of machine learning models for breast cancer classification. The study introduces a hybrid approach that combines the Gravitational Search Optimization Algorithm (GSOA) and Emperor Penguin Optimization (EPO) to identify influential features while eliminating irrelevant ones. The research aims to determine the minimum number of features needed to achieve higher accuracy in breast cancer classification. The experimental results demonstrate promising outcomes, with an accuracy of 97.66%, sensitivity of 0.9687, specificity of 1.000, precision of 1.000, F1-score of 0.9516, and area under the curve (AUC) of 0.9980. [Extracted from the article]

A recent report from the University College of Engineering in Nagercoil, India highlights the use of infrared thermography as an effective screening method for detecting breast cancer. The report discusses the application of deep learning algorithms in thermography to increase the accuracy of detection. The researchers propose an automated and optimized convolution neural network-based system for tumor segmentation of breast thermogram images, using a bio-inspired optimization technique called the mayfly algorithm. The results of the study suggest that this technique can assist physicians in making better decisions regarding breast cancer diagnosis. [Extracted from the article]

A recent study conducted by researchers at the University of Sydney explored the adaptability of artificial intelligence (AI) models to the Australian context of mammography for breast cancer diagnosis. The study examined the impact of image enhancement on model performance and analyzed the relationship between AI outputs and histopathological features. The results showed that AI models performed better when trained on a subset of mammograms with high concordance between radiologists' annotations and when image enhancement techniques were applied. The study concluded that AI models can enhance the accuracy of breast cancer diagnosis when certain factors, such as concordance classification, image enhancement, and transfer learning, are taken into account. [Extracted from the article]

A study conducted by researchers at Chengdu University of Traditional Chinese Medicine in Sichuan, China, explores the use of artificial intelligence in diagnosing breast cancer. The researchers propose a new method that utilizes a modified ZFNet network and an extreme learning machine (ELM) to enhance classification performance. The study compares the performance of their method to other well-known methods using various metrics and concludes that their approach, known as ZFNet-SWChOA-ELM, demonstrates the highest level of performance. This research has been peer-reviewed and provides valuable insights into the use of evolving deep convolutional neural networks for breast cancer diagnosis. [Extracted from the article]

Keywords: Bengaluru; India; Asia; Algorithms; Breast Cancer; Cancer; Cancer Detection; Health and Medicine; Machine Learning; Oncology; Software; Support Vector Machines; Women's Health EN Bengaluru India Asia Algorithms Breast Cancer Cancer Cancer Detection Health and Medicine Machine Learning Oncology Software Support Vector Machines Women's Health 209 209 1 05/08/23 20230509 NES 230509 2023 MAY 9 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Data detailed on Oncology - Cancer Detection have been presented. Bengaluru, India, Asia, Algorithms, Breast Cancer, Cancer, Cancer Detection, Health and Medicine, Machine Learning, Oncology, Software, Support Vector Machines, Women's Health. [Extracted from the article]

Keywords: Algorithms; Breast Cancer; Cancer; Genetics; Health and Medicine; Oncology; Women's Health EN Algorithms Breast Cancer Cancer Genetics Health and Medicine Oncology Women's Health 2023 MAR 7 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators publish new report on breast cancer. Algorithms, Breast Cancer, Cancer, Genetics, Health and Medicine, Oncology, Women's Health. [Extracted from the article]

Keywords: Baja California; Mexico; North and Central America; Algorithms; Breast Cancer; Cancer; Health and Medicine; Oncology; Women's Health EN Baja California Mexico North and Central America Algorithms Breast Cancer Cancer Health and Medicine Oncology Women's Health 2023 FEB 28 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Researchers detail new data in Oncology - Breast Cancer. Baja California, Mexico, North and Central America, Algorithms, Breast Cancer, Cancer, Health and Medicine, Oncology, Women's Health. [Extracted from the article]

A report from Technical and Vocational University (TVU) discusses the use of machine learning algorithms in predicting and diagnosing breast cancer. The researchers utilized several supervised machine learning algorithms, such as Logistic Regression, Decision Tree, Random Forest, and KNN, to analyze a dataset of 699 samples from the UCI repository. The study found that Logistic Regression and Extra Tree algorithms had the best performance in tumor classification, with accuracy values of 99.14% and AUC ROC values of 99.6% and 99.1% respectively. The researchers concluded that machine learning techniques can be useful in the diagnosis and prediction of breast cancer. [Extracted from the article]

A recent study conducted by researchers at Teikyo University School of Medicine in Japan focuses on the pre-operative differentiation of benign ovarian tumors (Be-OTs) based on magnetic resonance imaging (MRI) interpretation. The study analyzed the medical records of 901 patients with ovarian tumors and found that MRI interpretation was highly accurate in differentiating between Be-OTs and borderline/malignant ovarian tumors (Bo/Ma-OTs) in certain types of tumors, such as ovarian endometrial cyst and ovarian mature cystic teratoma. However, discrepancies between MRI and pathological findings were more common in serous/mucinous ovarian tumors. The researchers suggest that combining MRI interpretation with tumor size may improve differentiation between Be-OTs and Bo/Ma-OTs. Further research and analysis are needed to validate these findings. [Extracted from the article]

A report from the University Francisco of Vitoria in Madrid, Spain discusses the use of artificial intelligence (AI) in enhancing breast cancer diagnosis. The researchers evaluated different approaches using thermographic imaging and found that a hybrid model of convolutional neural networks and evolutionary algorithms achieved an accuracy of nearly 94%. The study highlights the potential benefits of computer vision and AI in medical diagnosis, particularly in the field of breast cancer screening. The research has been peer-reviewed and provides valuable insights into the application of AI in healthcare. [Extracted from the article]

A recent study conducted by researchers at Delhi Technological University in India has developed a new model for the early detection of breast cancer. The model, called 'hybrid artificial bee colony and black hole with genetic operators (GBHABC)', combines the techniques of two major algorithms and employs a support vector machine (SVM) ensemble technique. The researchers achieved an accuracy rate of 99.42% on the UCI dataset, surpassing previous literature in the field. This research provides a promising advancement in the early identification of breast cancer, which is crucial for preventing irreversible damage caused by the disease. [Extracted from the article]

Keywords: Ghent; Belgium; Europe; Algorithms; Breast Cancer; Cancer; Health and Medicine; Oncology; Women's Health EN Ghent Belgium Europe Algorithms Breast Cancer Cancer Health and Medicine Oncology Women's Health 1507 1507 1 11/06/23 20231109 NES 231109 2023 NOV 7 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- New research on Oncology - Breast Cancer is the subject of a report. Ghent, Belgium, Europe, Algorithms, Breast Cancer, Cancer, Health and Medicine, Oncology, Women's Health. [Extracted from the article]

For more information on this research see: Evolutionary Computing Model for Finding Breast Cancer Masses Using Image Enhancement Procedures With Artificial Intelligent Algorithms. Keywords: Tamil Nadu; India; Asia; Algorithms; Breast Cancer; Breast Cancer Screening; Cancer; Diagnostics and Screening; Health and Medicine; Mammogram; Mammography; Oncology; Risk and Prevention; Women's Health EN Tamil Nadu India Asia Algorithms Breast Cancer Breast Cancer Screening Cancer Diagnostics and Screening Health and Medicine Mammogram Mammography Oncology Risk and Prevention Women's Health 852 852 1 10/24/23 20231023 NES 231023 2023 OCT 24 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Current study results on Oncology - Breast Cancer have been published. [Extracted from the article]

Keywords: Algorithms; Breast Cancer; Cancer; Cancer Detection; Diagnostics and Screening; Drugs and Therapies; Genetics; Health and Medicine; Network Architectures; Networks; Neural Networks; Oncology; Personalized Medicine; Personalized Therapy; Women's Health EN Algorithms Breast Cancer Cancer Cancer Detection Diagnostics and Screening Drugs and Therapies Genetics Health and Medicine Network Architectures Networks Neural Networks Oncology Personalized Medicine Personalized Therapy Women's Health 738 738 1 09/25/23 20230926 NES 230926 2023 SEP 26 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Fresh data on personalized medicine are presented in a new report. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy.". [Extracted from the article]

Keywords: Bushehr; Iran; Algorithms; Breast Cancer; Cancer; Diagnostics and Screening; Emerging Technologies; Evolutionary Algorithm; Health and Medicine; Machine Learning; Mathematics; Networks; Neural Networks; Oncology; Perceptron; Women's Health EN Bushehr Iran Algorithms Breast Cancer Cancer Diagnostics and Screening Emerging Technologies Evolutionary Algorithm Health and Medicine Machine Learning Mathematics Networks Neural Networks Oncology Perceptron Women's Health 319 319 1 09/25/23 20230926 NES 230926 2023 SEP 26 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators publish new report on Oncology - Breast Cancer. Artificial Neural Networks (ANN) are a prevalent machine learning algorithm, which is very popular for prediction and classification problems. [Extracted from the article]

Algorithms, Breast Cancer, Cancer, Genetic Algorithms, Genetics, Health and Medicine, Oncology, Selection Algorithm, Women's Health Keywords: Algorithms; Breast Cancer; Cancer; Genetic Algorithms; Genetics; Health and Medicine; Oncology; Selection Algorithm; Women's Health EN Algorithms Breast Cancer Cancer Genetic Algorithms Genetics Health and Medicine Oncology Selection Algorithm Women's Health 648 648 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- New study results on breast cancer have been published. [Extracted from the article]

Algorithms, Breast Cancer Screening, Cancer, Diagnostics and Screening, Fuzzy Logic, Genetic Algorithms, Genetics, Health and Medicine, Mammogram, Mammography, Oncology, Risk and Prevention, Women's Health Keywords: Algorithms; Breast Cancer Screening; Cancer; Diagnostics and Screening; Fuzzy Logic; Genetic Algorithms; Genetics; Health and Medicine; Mammogram; Mammography; Oncology; Risk and Prevention; Women's Health EN Algorithms Breast Cancer Screening Cancer Diagnostics and Screening Fuzzy Logic Genetic Algorithms Genetics Health and Medicine Mammogram Mammography Oncology Risk and Prevention Women's Health 1478 1478 1 08/14/23 20230815 NES 230815 2023 AUG 15 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators publish new report on breast cancer screening. [Extracted from the article]

Algorithms, Breast Cancer Screening, Cancer, Diagnostics and Screening, Health and Medicine, Mammogram, Mammography, Oncology, Risk and Prevention, Women's Health Keywords: Algorithms; Breast Cancer Screening; Cancer; Diagnostics and Screening; Health and Medicine; Mammogram; Mammography; Oncology; Risk and Prevention; Women's Health EN Algorithms Breast Cancer Screening Cancer Diagnostics and Screening Health and Medicine Mammogram Mammography Oncology Risk and Prevention Women's Health 990 990 1 08/14/23 20230815 NES 230815 2023 AUG 15 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators discuss new findings in cancer. [Extracted from the article]

Keywords: Madhya Pradesh; India; Asia; Algorithms; Breast Cancer; Cancer; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Women's Health EN Madhya Pradesh India Asia Algorithms Breast Cancer Cancer Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Women's Health 299 299 1 07/24/23 20230728 NES 230728 2023 JUL 25 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Breast Cancer is now available. Madhya Pradesh, India, Asia, Algorithms, Breast Cancer, Cancer, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology, Women's Health. [Extracted from the article]

For more information on this research see: Novel Feature Selection Using Machine Learning Algorithm for Breast Cancer Screening of Thermography Images. Keywords: Uttar Pradesh; India; Asia; Algorithms; Breast Cancer; Cancer; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Women's Health EN Uttar Pradesh India Asia Algorithms Breast Cancer Cancer Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Women's Health 223 223 1 07/17/23 20230717 NES 230717 2023 JUL 18 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Data detailed on Oncology - Breast Cancer have been presented. [Extracted from the article]

Algorithms, Breast Cancer, Cancer, Health and Medicine, Oncology, Search Algorithms, Women's Health Keywords: Algorithms; Breast Cancer; Cancer; Health and Medicine; Oncology; Search Algorithms; Women's Health EN Algorithms Breast Cancer Cancer Health and Medicine Oncology Search Algorithms Women's Health 772 772 1 07/03/23 20230704 NES 230704 2023 JUL 4 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- New study results on breast cancer have been published. [Extracted from the article]

Algorithms, Breast Cancer, Cancer, Coronavirus, Health and Medicine, Oncology, RNA Viruses, Risk and Prevention, Viral, Virology, Women's Health Keywords: Algorithms; Breast Cancer; Cancer; Coronavirus; Health and Medicine; Oncology; RNA Viruses; Risk and Prevention; Viral; Virology; Women's Health EN Algorithms Breast Cancer Cancer Coronavirus Health and Medicine Oncology RNA Viruses Risk and Prevention Viral Virology Women's Health 783 783 1 05/29/23 20230529 NES 230529 2023 MAY 30 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators discuss new findings in breast cancer. [Extracted from the article]

Keywords: Algorithms; Breast Cancer Screening; Cancer; Department of Defense; Diagnostics and Screening; Gastroenterology; Genetics; Harvard Medical School; Health and Medicine; Information Technology; Mammogram; Mammography; Military and Defense; Oncology; Pancreas; Pancreatic Cancer; Pancreatic Neoplasms; Risk and Prevention; Ultrasound; Veterans; Veterans Health; Women's Health EN Algorithms Breast Cancer Screening Cancer Department of Defense Diagnostics and Screening Gastroenterology Genetics Harvard Medical School Health and Medicine Information Technology Mammogram Mammography Military and Defense Oncology Pancreas Pancreatic Cancer Pancreatic Neoplasms Risk and Prevention Ultrasound Veterans Veterans Health Women's Health 90 90 1 05/22/23 20230523 NES 230523 2023 MAY 23 (NewsRx) -- By a News Reporter-Staff News Editor at Immunotherapy Weekly -- An artificial intelligence tool has successfully identified people at the highest risk for pancreatic cancer up to three years before diagnosis using solely the patients' medical records, according to new research led by investigators at Harvard Medical School and the University of Copenhagen, in collaboration with VA Boston Healthcare System, Dana-Farber Cancer Institute, and the Harvard T.H. Chan School of Public Health. Next, the researchers tested the best performing algorithm on an entirely new set of patient records it had not previously encountered - a U.S. Veterans Health Administration data set of nearly 3 million records spanning 21 years and containing 3,864 individuals diagnosed with pancreatic cancer. [Extracted from the article]

Keywords: Riyadh; Saudi Arabia; Asia; Algorithms; Breast Cancer; Cancer; Health and Medicine; Oncology; Risk and Prevention; Women's Health EN Riyadh Saudi Arabia Asia Algorithms Breast Cancer Cancer Health and Medicine Oncology Risk and Prevention Women's Health 467 467 1 04/10/23 20230411 NES 230411 2023 APR 11 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Current study results on Oncology - Breast Cancer have been published. Riyadh, Saudi Arabia, Asia, Algorithms, Breast Cancer, Cancer, Health and Medicine, Oncology, Risk and Prevention, Women's Health Keywords for this news article include: Riyadh, Saudi Arabia, Asia, Algorithms, Breast Cancer, Cancer, Health and Medicine, Oncology, Risk and Prevention, Women's Health, Princess Nourah Bint Abdulrahman University. [Extracted from the article]

Keywords: Algorithms; Breast Cancer; Cancer; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Women's Health EN Algorithms Breast Cancer Cancer Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Women's Health 892 892 1 03/23/23 20230324 NES 230324 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Investigators publish new report on breast cancer. Algorithms, Breast Cancer, Cancer, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology, Women's Health. [Extracted from the article]