Search

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 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]

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]

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]

To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. Keywords: Algorithms; Cancer; Diagnostics and Screening; Health and Medicine; Machine Learning; Oncology; Prostate Cancer; Prostatic Neoplasms; Support Vector Machines EN Algorithms Cancer Diagnostics and Screening Health and Medicine Machine Learning Oncology Prostate Cancer Prostatic Neoplasms Support Vector Machines 1492 1492 1 09/11/23 20230912 NES 230912 2023 SEP 12 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators publish new report on prostate cancer. [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 study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [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]

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: Msila; Algeria; Algorithms; Cancer; Genetics; Health and Medicine; Oncology EN Msila Algeria Algorithms Cancer Genetics Health and Medicine Oncology 684 684 1 10/30/23 20231031 NES 231031 2023 OCT 31 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Current study results on Cancer have been published. This paper proposes a new bio-inspired algorithm for cancer classification in gene selection called Binary Grey Wolf Optimization Algorithm (BGWOA), which is based on hybridization between Minimum Redundancy-Maximum Relevance (MRMR) and a novel Binary Grey Wolf 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]

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]

For more information on this research see: Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Keywords: Algorithms; Artificial Intelligence; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Pathology; Prostate Cancer; Prostatic Neoplasms; Technology EN Algorithms Artificial Intelligence Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Pathology Prostate Cancer Prostatic Neoplasms Technology 575 575 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators discuss new findings in artificial intelligence. [Extracted from the article]

Keywords: Saint-Etienne; France; Europe; Algorithms; Cancer; Health and Medicine; Oncology; Sarcomas EN Saint-Etienne France Europe Algorithms Cancer Health and Medicine Oncology Sarcomas 410 410 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Research findings on Cancer are discussed in a new report. Saint-Etienne, France, Europe, Algorithms, Cancer, Health and Medicine, Oncology, Sarcomas. [Extracted from the article]

This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity. Keywords: Algorithms; Artificial Intelligence; Cancer; Emerging Technologies; Gastric Cancer; Gastroenterology; Health and Medicine; Machine Learning; Oncology EN Algorithms Artificial Intelligence Cancer Emerging Technologies Gastric Cancer Gastroenterology Health and Medicine Machine Learning Oncology 427 427 1 03/23/23 20230321 NES 230321 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- A new study on artificial intelligence is now available. [Extracted from the article]

Keywords: Algorithms; Cancer; DNA Damage; DNA Research; Deoxyribonucleic Acid; Esophageal Cancer; Gastroenterology; Genetics; Health and Medicine; Oncology; Proteomics; Rehabilitation; Technology; Therapy EN Algorithms Cancer DNA Damage DNA Research Deoxyribonucleic Acid Esophageal Cancer Gastroenterology Genetics Health and Medicine Oncology Proteomics Rehabilitation Technology Therapy 603 603 1 03/23/23 20230314 NES 230314 2023 MAR 14 (NewsRx) -- By a News Reporter-Staff News Editor at Immunotherapy Weekly -- Data detailed on esophageal cancer have been presented. This paper proposed a study on DNA damage repair of cancer cells based on goal directed fluid therapy combined with rehabilitation therapy, aiming to optimize the traditional treatment of esophageal cancer by using goal directed fluid therapy technology. [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]

According to the news editors, the research concluded: "This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data." Originality/value This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data." For more information on this research see: Cancer Data Classification By Quantum-inspired Immune Clone Optimization-based Optimal Feature Selection Using Gene Expression Data: Deep Learning Approach. [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]

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]

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]

This article discusses a study that compares seven different algorithms for tracing fibre-like structures in biomedical images. The analysis of these structures can help in understanding processes like angiogenesis and the effects of certain treatments. The study found that no single algorithm outperformed the others in all metrics, but the Trace Ridges algorithm ranked first in terms of total distance error. The algorithm is fully automatic and combines various image-processing techniques to accurately delineate the fibre-like structures. The article also provides information on where to access the code for the Trace Ridges algorithm. Please note that this preprint has not been peer-reviewed. [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]

Insilico Medicine, an AI-driven biotech company, has developed a groundbreaking algorithm called Quantum-assisted Fragment-based Automated Structure Generator (QFASG) that aids in automated drug design. QFASG uses advanced chemoinformatic techniques to create new small-molecule structures from a library of molecular fragments. The researchers successfully applied QFASG to generate new small-molecule inhibitors for two pharmacological targets involved in cancer pathogenesis. The algorithm's fully automated pipeline streamlines the process of structure generation and optimization, providing researchers with a powerful tool for accelerating the drug discovery process. [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]

Researchers from Sohag University have developed a new algorithm, called RBNRO-DE, for gene selection in cancer classification using high-dimensional data. The algorithm was tested on 22 cancer datasets and compared to other meta-heuristic algorithms. The results showed that the RBNRO-DE algorithm achieved optimal convergence and classification accuracy up to 100%, with a feature reduction size of 98%. This research highlights the potential of RNA sequencing as a technique for gene profiling and quantification in cancer research. [Extracted from the article]

A recent study by researchers at the University of Pennsylvania School of Medicine has examined the role of health care algorithms in perpetuating racial and ethnic disparities. The study found that removing race from a kidney function algorithm had unintended consequences, such as reducing chemotherapy access and eligibility for clinical trials for Black patients. The researchers emphasized the importance of intentionality in algorithm development and suggested strategies to mitigate disparities, including using non-race variables and refining algorithms to minimize disparities across groups. The study highlights the need for transparency and a race-aware approach in algorithm development to promote health care equity. [Extracted from the article]

A study conducted by researchers at the Nanyang Institute of Technology in Henan, China, proposes a new methodology for the early diagnosis of melanoma skin cancer using dermoscopic images. The method involves scaling and enhancing the quality of the images, extracting features based on the Gray-Level Co-occurrence Matrix (GLCM), and using a newly developed version of the Archimedes Optimization Algorithm (DAOA) to select the most relevant features. The selected features are then classified using a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method achieved high values for performance indicators and demonstrated an accuracy of 88%, sensitivity of 96%, specificity of 81%, precision of 97%, and F-measure of 97% for the diagnosis of melanoma. [Extracted from the article]

A recent study conducted by the School of Computer Science at Cairo University focused on the early detection of hepatocellular carcinoma (HCC), a type of liver cancer, in Egyptian patients. The study proposed a model that utilizes machine learning algorithms to examine circulating microRNAs (miRNAs) as diagnostic and predictive markers for HCC. The model consists of three phases: data preprocessing, feature selection using a new algorithm called Binary African Vulture Optimization Algorithm (BAVO), and classification with support vector machine and k-folds cross-validation. The results showed that miRNAs-483-5p, 21, and 155 may serve as potential biomarkers for the early detection and prognosis of HCC. [Extracted from the article]

A recent report from the School of Electrical Engineering in Tamil Nadu, India discusses the use of machine learning and computer vision in cancer classification. The researchers highlight the potential of these technologies in speeding up the diagnosis of cancer and improving treatment planning. The report focuses on nine major cancer types and examines the use of various imaging modalities and machine learning algorithms in their classification. The objective of the study is to provide researchers and medical experts with a comprehensive understanding of the current state of cancer classification using computer vision and machine learning, with the aim of enhancing cancer detection and therapy precision. [Extracted from the article]

Researchers from the University of Anbar have developed a deep learning algorithm using EfficientNet B3 for the detection of lung cancer. The algorithm aims to improve detection accuracy and potentially revolutionize medical imaging and patient care. The model was able to classify four different types of lung cancer with an accuracy rate of 96%, showing a 2.13% improvement compared to the best-trained classifier. The researchers believe that this model has the potential to enhance lung cancer detection and support early diagnosis. [Extracted from the article]

Keywords: Hefei; People's Republic of China; Asia; Clinical Oncology; Algorithms EN Hefei People's Republic of China Asia Clinical Oncology Algorithms 654 654 1 04/17/23 20230418 NES 230418 2023 APR 18 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Fresh data on Clinical Oncology are presented in a new report. Our news editors obtained a quote from the research from the Hefei University of Technology, "For complex and diverse lung nodule data sets, this paper proposes a new causal lung nodule detection algorithm Tau-CSFS in combination with rank correlation methods. [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]

This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. Keywords: Algorithms; Cancer; Health and Medicine; Lung Cancer; Lung Diseases and Conditions; Lung Neoplasms; Oncology EN Algorithms Cancer Health and Medicine Lung Cancer Lung Diseases and Conditions Lung Neoplasms Oncology 2023 MAR 7 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Data detailed on lung cancer have been presented. [Extracted from the article]

Keywords: New Delhi; India; Asia; Algorithms; Cancer; Cyborgs; Diagnostics and Screening; Emerging Technologies; Genetics; Health and Medicine; Lung Cancer; Lung Diseases and Conditions; Lung Neoplasms; Machine Learning; Oncology; Support Vector Machines; Vector Machines EN New Delhi India Asia Algorithms Cancer Cyborgs Diagnostics and Screening Emerging Technologies Genetics Health and Medicine Lung Cancer Lung Diseases and Conditions Lung Neoplasms Machine Learning Oncology Support Vector Machines Vector Machines 934 934 1 08/21/23 20230822 NES 230822 2023 AUG 22 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators discuss new findings in Oncology - Lung Cancer. Therefore, automated lung cancer detection and classification from radiological data aided with machine learning and deep learning techniques have become essential because it provides a precise, trustworthy, and rapid investigation of the condition's progression. [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]

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]

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 Delhi Technological University in Delhi, India, has developed a new algorithm-based technique for diagnosing breast cancer. The study utilized the Wisconsin Breast Cancer Dataset and employed both SVM and KNN algorithms for data analysis. The proposed hybrid algorithm, called SSA-GWO, achieved an accuracy of 99.42% when applied to the dataset. The research concludes that the hybrid algorithm shows promise for diagnosing breast cancer in challenging situations. [Extracted from the article]

A new report discusses research findings on cancer gene selection using omics data. Researchers at Henan University in Kaifeng, People's Republic of China, have developed a two-stage prior LASSO (TSPLASSO) algorithm for gene selection. The algorithm incorporates prior knowledge of cancer genes and performs sample classification and variable selection simultaneously. Numerical simulations show that TSPLASSO improves the accuracy of variable selection by 5%-400% in various datasets. The TSPLASSO algorithm is considered efficient, stable, and practical for exploring biomedical and health informatics from omics data. [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]

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]