AI Medical Compendium Topic:
Brain Neoplasms

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Survival Prediction After Neurosurgical Resection of Brain Metastases: A Machine Learning Approach.

Neurosurgery
BACKGROUND: Current prognostic models for brain metastases (BMs) have been constructed and validated almost entirely with data from patients receiving up-front radiotherapy, leaving uncertainty about surgical patients.

Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.

IEEE journal of translational engineering in health and medicine
BACKGROUND: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficie...

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

Computational intelligence and neuroscience
In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recogni...

Explainability of deep neural networks for MRI analysis of brain tumors.

International journal of computer assisted radiology and surgery
PURPOSE: Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restrictio...

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...