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

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

You might also read

Related Articles

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

Sort by

Same author

Advanced security in fog environments using encryption and adaptive user activity tracking.

Scientific reports·2026

Same author

ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection.

Cancers·2025

Same author

Diagnostic accuracy and safety of ultrasound-guided percutaneous core needle biopsy among children with extra cranial solid masses at a tertiary care hospital in Karachi, Pakistan.

Pakistan journal of medical sciences·2025

Same author

LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.

Bioengineering (Basel, Switzerland)·2025

Same author

Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network.

Sensors (Basel, Switzerland)·2024

Same author

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

Medical & biological engineering & computing·2024

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

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

Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model.

Aditya Pal¹, Hari Mohan Rai², Mohamed Ben Haj Frej³

¹Department of Information Technology, Dronacharya Group of Institutions, Greater Noida 201306, India.

Life (Basel, Switzerland)

|November 27, 2024

Summary

A new hybrid U-Net and Mask R-CNN model, U-MaskNet, enhances gastrointestinal cancer detection. This AI approach improves early diagnosis accuracy and patient care by providing superior segmentation and classification of GI diseases.

Keywords:

U-MaskNet model deep learning gastrointestinal cancer detection novel segmentation model performance evaluation visualizations

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

Related Experiment Videos

Last Updated: Jul 8, 2026

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Early diagnosis of gastrointestinal (GI) cancers is critical for patient outcomes.
Current diagnostic tools face challenges in efficiency and accuracy for early detection.
Accurate classification and segmentation of GI diseases are essential for timely intervention.

Purpose of the Study:

To develop an advanced approach for classifying and segmenting various GI cancer diseases.
To introduce a novel hybrid segmentation model, U-MaskNet, for improved GI cancer diagnostics.
To address the limitations of existing diagnostic tools for early GI cancer detection.

Main Methods:

Proposed a hybrid segmentation model, U-MaskNet, combining U-Net for pixel-wise classification and Mask R-CNN for instance segmentation.
Utilized the Kvasir dataset, comprising 8000 endoscopic images of GI cancers, for model validation.
Compared U-MaskNet's performance against established models like DeepLabv3+, FCN, DeepMask, LeNet-5, AlexNet, VGG-16, ResNet-50, and Inception Network.

Main Results:

U-MaskNet demonstrated superior segmentation performance compared to DeepLabv3+, FCN, and DeepMask.
Achieved improved classification performance over state-of-the-art models including ResNet-50 and Inception Network.
Quantitative analysis showed U-MaskNet achieved 98.85% precision, 98.49% recall, 98.68% F1 score, 94.35% Dice coefficient, and 89.31% IoU.

Conclusions:

The developed U-MaskNet model significantly enhances accuracy and reliability in detecting and segmenting GI cancers.
The integration of U-Net and Mask R-CNN models offers a promising solution for improving GI cancer diagnostics.
This research paves the way for enhanced clinical diagnostic processes and improved patient care through advanced medical image segmentation.