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Brain Neoplasms

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A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utili...

Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review.

International journal of molecular sciences
The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine lea...

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

Computers in biology and medicine
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range...

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification acc...

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation metho...

Optimising trajectory planning for stereotactic brain tumour biopsy using artificial intelligence: a systematic review of the literature.

British journal of neurosurgery
PURPOSE: Despite advances in technology, stereotactic brain tumour biopsy remains challenging due to the risk of injury to critical structures. Indeed, choosing the correct trajectory remains essential to patient safety. Artificial intelligence can b...

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.

Journal of neuro-oncology
PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has...

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

Sensors (Basel, Switzerland)
One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient's health. Many automated artificial intel...

Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning.

Journal of cancer research and clinical oncology
PURPOSE: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, ho...