Search research articles

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

A 24 GHz End-Fire Rod Antenna Based on a Substrate Integrated Waveguide.

Sensors (Basel, Switzerland)·2025

Same author

Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.

Biomedical engineering and computational biology·2024

Same author

Editorial for the Special Issue on Recent Advances in Microwave Components and Devices.

Micromachines·2024

Same author

En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.

Biomedicines·2023

Same author

Biomechanical Effects of the Porous Structure of Gyroid and Voronoi Hip Implants: A Finite Element Analysis Using an Experimentally Validated Model.

Materials (Basel, Switzerland)·2023

Same author

An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning.

Sensors (Basel, Switzerland)·2023

Same journal

RETRACTED: Sabir et al. DNA Based and Stimuli-Responsive Smart Nanocarrier for Diagnosis and Treatment of Cancer: Applications and Challenges. <i>Cancers</i> 2021, <i>13</i>, 3396.

Cancers·2026

Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026

Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026

Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026

Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026

Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Aug 13, 2025

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

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

Published on: November 30, 2022

APESTNet with Mask R-CNN for Liver Tumor Segmentation and Classification.

Prabhu Kavin Balasubramanian¹, Wen-Cheng Lai^2,3, Gan Hong Seng⁴

¹Department of Data Science and Business System, Kattankulathur Campus, SRM Institute of Science and Technology, Chennai 603203, Tamil Nadu, India.

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a novel deep learning model for precise liver tumor segmentation and classification using Computed Tomography (CT) images. The APESTNet model demonstrates superior performance and efficiency in identifying liver tumors, improving diagnostic accuracy.

Keywords:

adversarial propagation classification computed tomography enhanced swin transformer network liver tumor segmentation median filtering

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

Related Experiment Videos

Last Updated: Aug 13, 2025

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

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

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate liver tumor segmentation and classification are crucial for diagnosing and treating hepatocellular carcinoma and metastases.
Challenges in liver tumor analysis include indistinct borders and variations in shape, size, and position.
Advancements in artificial intelligence, particularly transformer models, offer new possibilities for computer vision tasks.

Purpose of the Study:

To develop and evaluate a novel deep learning model for automated liver tumor segmentation and classification.
To improve the precision of liver tumor analysis beyond existing clinical methods.
To apply a three-stage deep learning approach for enhanced diagnostic capabilities.

Main Methods:

A three-stage process involving pre-processing, liver segmentation, and classification was employed.
Computed Tomography (CT) images underwent contrast enhancement (histogram equalization) and noise reduction (median filter).
An enhanced Mask R-CNN model performed liver segmentation, followed by an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet) for classification and overfitting prevention.

Main Results:

The proposed APESTNet model demonstrated superior performance in segmenting and classifying liver tumors across a variety of CT images.
The model proved to be efficient and exhibited low sensitivity to noise in the CT scans.
Experimental results validated the effectiveness of the deep learning approach for liver tumor analysis.

Conclusions:

The novel deep learning model, APESTNet, offers a highly effective and efficient solution for liver tumor segmentation and classification.
The proposed method enhances diagnostic accuracy for hepatocellular carcinoma and metastases.
The model's robustness to noise and performance on diverse CT images highlight its clinical potential.