Jove
Visualize
Contact Us

Related Concept Videos

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.1K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.1K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.7K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.7K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.2K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.2K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Classification of Leukocytes01:30

Classification of Leukocytes

5.7K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
5.7K
Classification of Bones01:18

Classification of Bones

9.8K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
9.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Evaluating the real-world robustness of face-swap detection models under compression and noise.

Frontiers in artificial intelligence·2026
Same journal

Editorial: AI and resilience.

Frontiers in artificial intelligence·2026
Same journal

LungCraft: a hybrid 3D-2D deep learning and radiomics framework with explainable AI for precision diagnosis of lung cancer.

Frontiers in artificial intelligence·2026
Same journal

Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.

Frontiers in artificial intelligence·2026
Same journal

Identification of key sentences in a text.

Frontiers in artificial intelligence·2026
Same journal

Scale, trust, and the digital divide: a systematic review of AI and ML for agricultural applications.

Frontiers in artificial intelligence·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 28, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.5K

Explainable AI-driven MRI-based brain tumor classification: a novel deep learning approach.

Vinayaka R Srinivas1, Ramasubramanian Parvathi1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in Artificial Intelligence
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

A novel deep learning framework efficiently classifies brain tumors from MRI scans with 95.86% accuracy. This approach shows promise for improved diagnostic tools in clinical settings.

Keywords:
MRIbrain tumor classificationconvolutional neural networksdata augmentationdeep learningexplainable AIfeature visualizationmedical imaging

More Related Videos

Lateral Molar Approach-Driven Transoral Endoscopic Procedure for Benign Infratemporal Fossa Tumor Resection
04:04

Lateral Molar Approach-Driven Transoral Endoscopic Procedure for Benign Infratemporal Fossa Tumor Resection

Published on: August 15, 2025

398
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.7K

Related Experiment Videos

Last Updated: Jan 28, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.5K
Lateral Molar Approach-Driven Transoral Endoscopic Procedure for Benign Infratemporal Fossa Tumor Resection
04:04

Lateral Molar Approach-Driven Transoral Endoscopic Procedure for Benign Infratemporal Fossa Tumor Resection

Published on: August 15, 2025

398
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.7K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors represent a significant oncological challenge, necessitating accurate diagnostic methods.
  • Current diagnostic processes require improvement for better patient outcomes.

Purpose of the Study:

  • To develop an efficient deep learning framework for brain tumor classification using MRI data.
  • To achieve high accuracy in differentiating between normal tissue and various brain tumor types (glioma, pituitary, meningioma).

Main Methods:

  • Utilized Convolutional Neural Networks (CNNs), including DenseNet50 and VGG19 architectures.
  • Applied preprocessing techniques such as noise reduction, resizing, and data augmentation.
  • Employed Explainable AI (XAI) methods like Grad-CAM and LIME for model interpretability.

Main Results:

  • A 4-conv-1-dense-1-dropout CNN model achieved a classification accuracy of 95.86%.
  • The developed CNN model outperformed deeper architectures and transfer learning models.
  • XAI techniques provided insights into the model's decision-making process.

Conclusions:

  • Deep learning models offer a reliable and efficient solution for brain tumor classification.
  • The study recommends real-time clinical deployment and future integration with Large Language Models (LLMs) for automated reporting.