Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals.

Scientific reports·2026

Same author

Assessment of deep morphology and cryodamage in spermatozoa of indigenous and exotic pig breeds from the Northeast Indian Himalayas using scanning electron microscopy (SEM).

Open veterinary journal·2026

Same author

Experimentally validated dual-band GHz metamaterial perfect absorber biosensor with negative-index response and AI-assisted electromagnetic analysis for breast cancer dielectric discrimination.

Biosensors & bioelectronics·2026

Same author

Living with spinal cord injury in the community of Bangladesh: a comprehensive analysis using the ICF framework.

Journal of rehabilitation medicine·2026

Same author

A compact triple wideband mimo antenna for microwave, ku, and mm-wave band applications of 5g wireless communication.

PloS one·2026

Same author

First-principles study of rare-earth-free Cs<sub>4</sub>SrI<sub>6</sub>:Tl, a zero-dimensional halide perovskite for scintillation applications.

RSC advances·2026

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor

Amran Hossain¹, Rafiqul Islam¹, Mohammad Tariqul Islam²

¹Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, Gazipur, 1707, Bangladesh.

Computers in Biology and Medicine

|November 3, 2024

Summary

A new deep transfer learning model, FT-FEDTL, accurately classifies microwave brain tumor images. This AI approach aids radiologists in early brain tumor detection and diagnosis.

Keywords:

Brain tumor classification Deep convolutional neural network Feature extraction Fine-tuning Microwave-based tumor image Transfer learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jun 8, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Biomedical Engineering
Medical Imaging Analysis
Artificial Intelligence in Healthcare

Background:

Microwave brain imaging (MBI) is an emerging technology for early brain tumor detection.
Manual classification of microwave-based brain tumors (MBT) is time-consuming and challenging for physicians.
Deep transfer learning (DTL) offers potential for efficient brain tumor classification.

Purpose of the Study:

To propose an efficient DTL model, FT-FEDTL, for multi-class MBT classification.
To enhance automated identification and categorization of MBT images for improved brain tumor diagnosis.
To provide a superior pathway for brain tumor diagnosis using advanced AI.

Main Methods:

Utilized the InceptionV3 architecture as a base for feature extraction in the FT-FEDTL model.
Applied fine-tuning to five additional layers with hyperparameters for enhanced classification performance.
Created a balanced dataset of 4200 MBT images using augmentation techniques from two sources.

Main Results:

The FT-FEDTL model achieved superior classification performance on a balanced dataset compared to traditional and pre-trained models.
Attained high performance metrics: 99.65% accuracy, 99.16% recall, 99.48% precision, 99.10% specificity, and 99.23% F-score.
Demonstrated effectiveness in classifying six classes of brain tumors.

Conclusions:

The FT-FEDTL model shows significant potential for assisting radiologists in multi-class MBT image classification.
The proposed model offers a reliable and efficient tool for biomedical applications in brain tumor diagnosis.
Experimental outcomes validate the model's capability for accurate and automated tumor classification.