AIMC Topic: Brain Neoplasms

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A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Sensors (Basel, Switzerland)
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, an...

A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans.

Computers in biology and medicine
AIM OF STUDY: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing...

MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation.

Medical & biological engineering & computing
The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underutilize multiscale features, which can easily lead to loss of contextual information, reduction of low...

Radiomics in neuro-oncological clinical trials.

The Lancet. Digital health
The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the...

CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.

Magnetic resonance in medicine
PURPOSE: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction.

Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images.

Clinical radiology
AIM: To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images.

Super-resolution of brain tumor MRI images based on deep learning.

Journal of applied clinical medical physics
INTRODUCTION: To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors.

Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach.

Computational intelligence and neuroscience
The development of unusual cells in the cerebrum causes brain cancer. It is classified primarily into two classes: a noncarcinogenic (benign) type of growth and cancerous (malignant) growth. Early detection of this disease is a quintessential task fo...

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

Scientific reports
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the acc...

Region Convolutional Neural Network for Brain Tumor Segmentation.

Computational intelligence and neuroscience
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close s...