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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 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 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 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]

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 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 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]