Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410780PMC
http://dx.doi.org/10.1155/2022/2980691DOI Listing

Publication Analysis

Top Keywords

brain tumor
12
brain tumors
12
brain
8
cnn model
8
model densenet-169
8
choices images
8
growth
5
metaheuristic optimization-driven
4
optimization-driven novel
4
novel deep
4

Similar Publications

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that remains an unmet medical need. Because TNBC cells do not express the most common markers of breast cancers, there is an active search for novel molecular targets in triple-negative tumors. Additionally, this subtype of breast cancer presents strong immunogenic characteristics which have been encouraging the development of immunotherapeutic approaches against the disease.

View Article and Find Full Text PDF

CircPRKD3-loaded exosomes concomitantly elicit tumor growth inhibition and glioblastoma microenvironment remodeling via inhibiting STAT3 signaling.

Neuro Oncol

January 2025

Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, P.R. China.

Background: Glioblastoma stem cells (GSCs) and their exosomes (exos) are involved in shaping the immune microenvironment, which is important for tumor invasion and recurrence. However, studies involving GSC-derived exosomal circular RNAs (GDE-circRNAs) in regulating tumor microenvironment (TME) remain unknown. Here, we comprehensively evaluated the significance of a novel immune-related GDE-circRNA in glioma microenvironment.

View Article and Find Full Text PDF

Glioblastoma (GBM) is described as a group of highly malignant primary brain tumors and stands as one of the most lethal malignancies. The genetic and cellular characteristics of GBM have been a focal point of ongoing research, revealing that it is a group of heterogeneous diseases with variations in RNA expression, DNA methylation, or cellular composition. Despite the wealth of molecular data available, the lack of transferable pre-clinic models has limited the application of this information to disease classification rather than treatment stratification.

View Article and Find Full Text PDF

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This work combines machine learning and single-cell RNA sequencing (scRNA-seq) to explore the glioma tumour microenvironment's TME. With the help of genome-wide association studies (GWAS) and Mendelian randomization (MR), we found genetic variants associated with TME elements that affect cancer and cardiovascular disease outcomes.

View Article and Find Full Text PDF

Purpose: Glioblastoma multiforme (GBM) is an aggressive brain tumor. This meta-analysis investigates the association between HOTAIR expression levels and GBM.

Methods: We searched the literature for studies on HOTAIR expression in GBM patients.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!