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A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

A report from Kerman University of Medical Sciences discusses the use of natural language processing (NLP) to extract cancer concepts from clinical notes automatically. The study systematically reviewed papers that used NLP methods to identify cancer concepts from clinical notes. The researchers found that rule-based algorithms were the most commonly used for concept extraction, and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies. The study concluded that the use of NLP for extracting cancer concepts has increased in recent years, and suggested that future studies use rule-based algorithms for extracting concepts of other diseases as well. [Extracted from the article]

A recent study conducted at the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

According to the news editors, the research concluded: "The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image." To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm.". [Extracted from the article]

Keywords: Algorithms; Gliomas; Health and Medicine; Oncology EN Algorithms Gliomas Health and Medicine Oncology 249 249 1 10/09/23 20231010 NES 231010 2023 OCT 9 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Research findings on gliomas are discussed in a new report. According to the news editors, the research concluded: "This indicates that the algorithm proposed in this paper is an effective automatic glioma segmentation algorithm.". [Extracted from the article]

A recent report from Universiti Malaya discusses research on cancer treatment using optimal control supported by swarm intelligence. The study aims to develop hybrid techniques that combine optimal control theory with evolutionary algorithms and multi-objective swarm algorithms to reduce cancerous cells while minimizing the use of chemotherapy medications and toxicity. Two hybrid techniques were proposed and evaluated, with MOEA/D emerging as the most effective algorithm in reducing tumor size and cancer drug concentration. The study highlights the importance of integrating optimal control theory and evolutionary algorithms for optimizing cancer chemotherapy treatment. [Extracted from the article]

A recent report from the School of Electronics Engineering discusses research on brain cancer and the challenges of accurately separating tumor regions from healthy tissue in medical imaging. The researchers propose a novel semantic segmentation architecture called IC-Net, which combines elements from various models to improve brain tumor segmentation in MRI images. The IC-Net design outperforms other segmentation techniques, achieving notable outcomes in accuracy, loss, specificity, sensitivity, and Dice similarity coefficient. This research has the potential to enhance the accuracy of brain tumor segmentation and improve medical imaging analysis in the field of oncology. [Extracted from the article]

A research study conducted at the KLS Gogte Institute of Technology in Belagavi, India, has explored the use of super-resolution (SR) reconstruction techniques for improving the spatial resolution of pathology images in prostate cancer diagnosis. The study compared various machine learning, deep learning, and generative adversarial network (GAN) algorithms and evaluated their performance using different metrics. The results showed that the SRGAN algorithm outperformed other algorithms, achieving high-quality image resolution and fine details. The application of SRGAN could provide a cost-effective solution for enhancing the accuracy of cancer diagnosis by improving the visualization of cellular and tissue structures in pathology images. [Extracted from the article]

A research study conducted in Bucharest, Romania, analyzed the management of ectopic mediastinal thyroid (EMT) in relation to cancer and non-malignant findings. The study reviewed 89 original articles and classified them into four groups: studies/case series, malignant EMTs, benign EMTs without thyroid anomalies, and benign EMTs with thyroid anomalies. The researchers proposed a 10-item algorithm to navigate the EMT domain. The study found that the clinical index of suspicion for EMT remains low, but the rate of cancer is higher than previously reported. Surgery was found to have a good overall outcome. [Extracted from the article]

A recent report from the North China University of Science and Technology discusses the use of the Internet of Medical Things (IoMT) in cancer diagnosis and treatment. The report introduces a new algorithm called Dependent Nearest Neighbor Powered Twin Support Vector Machine (DTWSVM) for predicting cancer recurrence. The algorithm achieved an average accuracy of 91.53% across seven cancer datasets, outperforming other existing models. The researchers suggest that the DTWSVM algorithm could improve prediction accuracy and assist clinicians in making treatment decisions. [Extracted from the article]

Researchers from the College of Animal Sciences and Technology in Tongliao, China have developed a novel machine learning method for the early detection of brain tumors. The method combines the advantages of Gated Recurrent Unit (GRU) networks and the Enhanced Hybrid Dwarf Mongoose Optimization (EHDMO) algorithm to analyze MRI images and accurately detect brain tumors. The proposed method achieved high sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, outperforming other recent methods. This research provides a promising approach to automate and improve brain tumor detection using advanced technology. [Extracted from the article]

Researchers from Hebei University in Baoding, China have developed a new algorithm for diagnosing neuroblastoma, a type of malignant solid tumor in children. The algorithm uses a network model based on the U-Net structure, with DenseNet as the encoder, to extract features from whole slide images of neuroblastoma. The algorithm achieved high accuracy in segmenting and counting poorly differentiated and differentiated cells, which can help determine the histopathological types and prognostic effects of neuroblastoma. The research has been peer-reviewed and published in the journal Signal Image and Video Processing. [Extracted from the article]

Keywords: Algorithms; Cancer; Cyborgs; Emerging Technologies; Genetics; Health and Medicine; Machine Learning; Oncology EN Algorithms Cancer Cyborgs Emerging Technologies Genetics Health and Medicine Machine Learning Oncology 2023 MAR 14 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Fresh data on cancer are presented in a new report. Algorithms, Cancer, Cyborgs, Emerging Technologies, Genetics, Health and Medicine, Machine Learning, Oncology. [Extracted from the article]

Tamil Nadu, India, Asia, Algorithms, Cancer, Cancer Detection, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology, Skin Cancer, Skin Neoplasms Keywords: Tamil Nadu; India; Asia; Algorithms; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Skin Cancer; Skin Neoplasms EN Tamil Nadu India Asia Algorithms Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Skin Cancer Skin Neoplasms 732 732 1 09/19/23 20230919 NES 230919 2023 SEP 19 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on Oncology - Cancer Detection. [Extracted from the article]

A recent study conducted at Anhui University in China has proposed a new algorithm called the adaptive conjugate gradient accelerated evolutionary algorithm (ACG-EA) for optimizing cancer treatment plans in intensity-modulated proton therapy. This technique is considered one of the most advanced cancer treatment methods due to the unique characteristics of proton radiation. The ACG-EA algorithm aims to generate diverse high-quality treatment plans simultaneously, addressing the limitations of existing optimization methods. Experimental results have shown promising performance in six clinical cancer cases. This research has been peer-reviewed and published in the journal Applied Soft Computing. [Extracted from the article]

Researchers from HITEC University in Taxila, Pakistan have developed a framework for classifying brain tumors using MRI scans. The framework utilizes a hybrid deep learning model and an optimal information fusion framework to improve accuracy. The researchers addressed the challenge of imbalanced training data by using a sparse autoencoder network to generate new images. They also utilized Bayesian optimization to initialize the hyperparameters of two pretrained neural networks. The proposed framework achieved an accuracy of 99.80% and showed improvement compared to existing methods. [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]

Researchers from Tehran University of Medical Sciences have developed a new approach for automating the segmentation of glioblastoma multiforme (GBM) tumors from MRI images. The technique utilizes the Marker-controlled watershed segmentation (MCWS) algorithm, along with other image processing techniques, to improve segmentation accuracy. The study highlights the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumors. The MCWS technique is considered an effective and efficient approach for segmenting challenging medical images. [Extracted from the article]

Researchers from Coimbatore, India have developed a two-phase deep learning-based framework to recognize and classify brain cancers in magnetic resonance images (MRIs). The first phase utilizes a new DTA approach to identify tumor MRI images from healthy individuals, while the second phase employs a hybrid features fusion-based brain tumor classification technique. The researchers evaluated several segmentation algorithms for MRI and PET scans, including Ant Colony Optimization, Wavelet Transform, Gradient Vector Flow, Gray level Co-occurrence matrix, and Artificial Bee Colony. The study found that the GVF strategy performed better with MRI images, while the ABC and ACO approaches performed similarly with PET scans. [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]

A study conducted by researchers at VIT-AP University in Andhra Pradesh, India, has developed a deep learning algorithm called RU-Net2+ for accurate brain tumor segmentation and survival rate prediction. The algorithm was applied to a dataset of brain tumor images and achieved impressive results, surpassing current benchmarks in classification accuracy, tumor segmentation precision, and survival rate prediction. The framework shows promise for automating brain tumor diagnosis and enhancing patient care, providing valuable insights for medical professionals making treatment decisions. The study was published in IEEE Access. [Extracted from the article]

The experimental results show that compared with the traditional convolutional neural network bone tumor image segmentation algorithm, the algorithm has a great improvement in segmentation accuracy and stability, the average Dice can reach 91.56%, the real-time performance is better." Keywords: Algorithms; Bone Cancer; Bone Research; Bone Tumors; Cancer; Conditional Random Fields; Convolutional Network; Emerging Technologies; Health and Medicine; Hematology; Machine Learning; Networks; Neural Networks; Oncology EN Algorithms Bone Cancer Bone Research Bone Tumors Cancer Conditional Random Fields Convolutional Network Emerging Technologies Health and Medicine Hematology Machine Learning Networks Neural Networks Oncology 250 250 1 10/03/23 20231003 NES 231003 2023 OCT 2 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on bone cancer. [Extracted from the article]

For more information on this research see: Glioma Grade Classification Using CNNs and Segmentation With an Adaptive Approach Using Histogram Features in Brain MRIs. Keywords: Algorithms; Gliomas; Health and Medicine; Oncology EN Algorithms Gliomas Health and Medicine Oncology 815 815 1 06/19/23 20230620 NES 230620 2023 JUN 19 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Current study results on gliomas have been published. [Extracted from the article]

The proposed framework using machine-learning-classification algorithms with the LIME method can be applied to find responsible genes to diagnose and treat HCC patients." Keywords: Algorithms; Cancer; Cyborgs; Emerging Technologies; Genetics; Health and Medicine; Liver Cancer; Machine Learning; Oncology; Risk and Prevention EN Algorithms Cancer Cyborgs Emerging Technologies Genetics Health and Medicine Liver Cancer Machine Learning Oncology Risk and Prevention 848 848 1 05/29/23 20230530 NES 230530 2023 MAY 30 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Researchers detail new data in liver cancer. [Extracted from the article]

Keywords: Gwangju; South Korea; Asia; Algorithms; Brain Cancer; Cancer; Diagnostics and Screening; Health and Medicine; Oncology EN Gwangju South Korea Asia Algorithms Brain Cancer Cancer Diagnostics and Screening Health and Medicine Oncology 729 729 1 05/02/23 20230502 NES 230502 2023 MAY 1 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on Oncology - Brain Cancer. Gwangju, South Korea, Asia, Algorithms, Brain Cancer, Cancer, Diagnostics and Screening, Health and Medicine, Oncology. [Extracted from the article]

Keywords: Xi'an; People's Republic of China; Asia; Algorithms; Brain Cancer; Cancer; Health and Medicine; Oncology EN Xi'an People's Republic of China Asia Algorithms Brain Cancer Cancer Health and Medicine Oncology 701 701 1 04/03/23 20230403 NES 230403 2023 APR 3 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- A new study on Oncology - Brain Cancer is now available. Xi'an, People's Republic of China, Asia, Algorithms, Brain Cancer, Cancer, Health and Medicine, Oncology. [Extracted from the article]

Algorithms, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology Keywords: Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology EN Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology 2023 MAR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- A new study on artificial intelligence is now available. [Extracted from the article]

Keywords: Esfahan; Iran; Algorithms; Cancer; Health and Medicine; Metaheuristic Algorithm; Networks; Neural Networks; Oncology; Optimization Algorithms EN Esfahan Iran Algorithms Cancer Health and Medicine Metaheuristic Algorithm Networks Neural Networks Oncology Optimization Algorithms 2023 MAR 14 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Current study results on Cancer have been published. Keywords for this news article include: Esfahan, Iran, Algorithms, Cancer, Health and Medicine, Metaheuristic Algorithm, Networks, Neural Networks, Oncology, Optimization Algorithms, Islamic Azad University. [Extracted from the article]

Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models." Keywords: Algorithms; Biomarkers; Cancer; Cyborgs; Diagnostics and Screening; Emerging Technologies; Glioblastomas; Health and Medicine; Machine Learning; Oncology EN Algorithms Biomarkers Cancer Cyborgs Diagnostics and Screening Emerging Technologies Glioblastomas Health and Medicine Machine Learning Oncology 2023 FEB 20 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Data detailed on glioblastomas have been presented. [Extracted from the article]