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A recent report from the Saveetha School of Engineering in Chennai, India, discusses the development of a deep learning framework using a convolution neural network algorithm for breast cancer detection and classification. Breast cancer is a significant health concern globally, affecting both men and women. The proposed framework aims to improve the efficiency and accuracy of breast cancer diagnosis by automatically analyzing breast images. The research concludes that the deep learning framework, specifically the convolution neural network, is highly reliable compared to existing algorithms. This research has been peer-reviewed and provides valuable insights into the field of cancer detection. [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]

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 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]

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 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]

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 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]