Jove
Visualize
Contact Us

Related Concept Videos

Classification of Illness01:17

Classification of Illness

9.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence and machine learning-driven design of self-healing biomedical composites.

Expert review of medical devices·2025
Same author

Automatic nutrient estimator: distributing nutrient solution in hydroponic plants based on plant growth.

PeerJ. Computer science·2024
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: Apr 30, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Joint classification and regression with deep multi task learning model using conventional based patch extraction for

Padmapriya K1, Ezhumalai Periyathambi1

  • 1Department of Computer Science and Engineering, RMD Engineering College, Chennai, Tamil Nadu, India.

Peerj. Computer Science
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

A novel Deep Multi-Task Convolutional Neural Network (DMTCNN) accurately diagnoses brain diseases using MRI scans. This AI model improves diagnostic accuracy and efficiency for better patient care.

Keywords:
Brain diseaseClassificationConvolutional neural networksDeep multi-task convolutional neural networkMagnetic resonance imagingMulti task learningRegression

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Related Experiment Videos

Last Updated: Apr 30, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Neurology and Diagnostic Medicine

Background:

  • Accurate diagnosis of brain diseases is crucial for effective treatment planning and patient care, with Magnetic Resonance Imaging (MRI) playing a key role.
  • Current computer-aided diagnosis methods often rely on manually engineered features from MRI, limiting their efficiency and adaptability.
  • Deep Multi-Task Convolutional Neural Network (DMTCNN) is proposed to overcome these limitations in brain disease diagnosis.

Purpose of the Study:

  • To develop and validate an effective iterative method for non-smooth optimization problems in brain disease diagnosis.
  • To present a Deep Multi-Task Convolutional Neural Network (DMTCNN) for combined regression and classification of brain diseases.
  • To enhance the utilization of shared information and improve overall performance through cooperative learning of multiple diagnostic tasks.

Main Methods:

  • Utilized a Deep Multi-Task Convolutional Neural Network (DMTCNN) for simultaneous regression and classification tasks related to brain diseases.
  • Employed an edge detector, specifically the Canny edge detector, for pre-processing MRI images.
  • Extracted discriminative anatomical features from image patches using convolutional neural networks (CNNs) within a multi-task learning (MTL) framework.

Main Results:

  • The DMTCNN model demonstrated significant improvements in key performance metrics, including specificity (94.18%), sensitivity (93.19%), accuracy (96.97%), and F1-score (94.18%).
  • Achieved a Root Mean Square Error (RMSE) of 22.76% and an execution time of 4.875 ms, showcasing high efficiency.
  • Verified the capability of DMTCNN to accurately categorize dissimilar brain disorders, outperforming state-of-the-art techniques.

Conclusions:

  • The proposed DMTCNN is an effective and efficient method for the computer-aided diagnosis of brain diseases using MRI.
  • Multi-task learning enhances model generalization and diagnostic accuracy, even with limited labeled data.
  • DMTCNN shows superior performance in precisely identifying various brain diseases compared to existing methods.