Advanced searches left 3/3

Brain Tumor - Wiley Online Library

Summarized by Plex Scholar
Last Updated: 10 June 2022

* If you want to update the article please login/register

Brain tumor classification by using a novel convolutional neural network structure

"Brain tumors in the skull are one of the health issues that cause significant consequences. " Rapid and reliable diagnosis of brain tumor types will ensure that the patient receives optimal treatment in the early period, thus improving the patient's chance of recovery and survival. If the DNN has multiple hyper-u201parameters, the coarse structure will be determined in a long time. Complex DNN structures or training algorithms in terms of practical use are impossible to develop in terms of practical use, because these techniques necessitate a lot of memory and high CPU computation. We have developed a new DNN system to improve classification accuracy, decrease the number of weights in the model, and reduce the number of hypermeters used in this study, as well as decreasing the number of hyperu2010parameters. The Figshare dataset's 99. 18% classification accuracy is obtained by using the small-u2010-sized DNN network without any pre-u2010processing stage or augmentation process, according to the author.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/ima.22763


Detection of brain tumor size using modified deep learning and multilevel thresholding utilizing modified dragonfly optimization algorithm

"Nowadays, brain tumor formation, or the abnormal growth of the mass of tissues in the human brain, is the leading cause of death among children and adults. " The Histogram Clipping Limited Adaptive Histogram Equalization scheme is used for enhancing the contrast of the inputted image, and certain aspects are deleted as a result of those contrast-u2010enhanced photos. With the support of MDNN, the image is categorized as tumor image and nontumor image. "Using MDu2010MT," the tumor portion is segmented as the classification tumor-inhibited photograph was divided as the distinctive tumor affected image on the classified tumor affected image.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/cpe.7016


Brain tumor detection and patient survival prediction using U‐Net and regression model

"Brain tumor segmentation is needed to determine the presence of tumor growth in a brain for future treatment planning. " This paper also introduces a new version of a regression model based on an Information set to estimate the survival rates of patients with a brain tumor. "Using the ubiquitous learning system based on the pervasive information set, the regression model's weights were determined. ".

Source link: https://onlinelibrary.wiley.com/doi/10.1002/ima.22735


Methylation classifiers: Brain tumors, sarcomas, and what's next

"In comparison, DNA methylation profiling has been used in a short time as a game changer with lasting effects on brain tumor classification and the possibility of assigning to other tumor types. " This essay provides a brief overview of DNA methylation characterization in u2010-based tumor classification. We describe why DNA methylation signatures are useful diagnostic biomarkers, review current and future objectives, and describe how methylation u2010based classifiers can be integrated into diagnostic procedures. U2010-based tumor profiling provides an insight into the challenges and opportunities surrounding DNA methylation.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/gcc.23041


Inhibition of EGFR and MEK surmounts entrectinib resistance in a brain metastasis model of NTRK1‐rearranged tumor cells

"Abstract Tropomyosin receptor kinase inhibitors have demonstrated histology u2010agnostic effect in patients with neurotrophic receptor kinase gene fusion fusion, according to a survey. " Although responses to TRK inhibitors can be immediate and durable, endurance of response may be limited by acquired resistance through a variety of mechanisms, including resistance mutations like NTRK1&u2010G595R. Repotrectinib, a second-generation TRK inhibitor, is also effective against NTRK1u2010G595R. We established entrectinib-u2010resistant tumor cells in a brain metastasis model inoculated with NTRK1u2010rearranged KM12SM cells and investigated the sensitivity of M3B cells to repotentinib in the present study. Although M3B cells possessed the NTRK1u2010G595R mutation, they were nonetheless immune to repotentinib, which was unexpectedly resistant to repotrectinib. We also show that the triplet combination of repotentinib, EGFR inhibitor, and MEK inhibitor can sensitize M3B cells in vitro as well as in a brain metastasis model.

Source link: https://onlinelibrary.wiley.com/doi/10.1111/cas.15354

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions