AIMC Topic: Brain Neoplasms

Clear Filters Showing 621 to 630 of 1218 articles

Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis.

BioMed research international
Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient pro...

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Scientific reports
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural n...

A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.

Scientific reports
Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the diffi...

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Computational intelligence and neuroscience
Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have pr...

Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In t...

Brain tumor classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison with AlexNet.

Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique
AIM: This research work aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from a magnetic resonance imaging (MRI) dataset. We developed a new MIDNet18 CNN architecture in comparison with ...

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors.

Computational and mathematical methods in medicine
The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based ...

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images.

Journal of healthcare engineering
Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various...

Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System.

IEEE journal of biomedical and health informatics
In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via t...