AIMC Topic: Brain Neoplasms

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Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools.

Computers in biology and medicine
BACKGROUND: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients s...

Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.

Medical physics
PURPOSE: The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise...

Efficient framework for brain tumor detection using different deep learning techniques.

Frontiers in public health
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals...

A Bibliometric Review: Brain Tumor Magnetic Resonance Imagings Using Different Convolutional Neural Network Architectures.

World neurosurgery
BACKGROUND: Numerous scientists and researchers have been developing advanced procedures and methods for diagnosing the kind and phase of a human tumor. Brain tumors, which are neoplastic and abnormal developments of brain cells, are one of the most ...

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Sensors (Basel, Switzerland)
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possib...

Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis.

Sensors (Basel, Switzerland)
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that...

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Journal of digital imaging
Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RAN...

Deep learning-based detection algorithm for brain metastases on black blood imaging.

Scientific reports
Brain metastases (BM) are the most common intracranial tumors, and their prevalence is increasing. High-resolution black-blood (BB) imaging was used to complement the conventional contrast-enhanced 3D gradient-echo imaging to detect BM. In this study...

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

Biomedical physics & engineering express
The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual ...