Jove
Visualize
Contact Us
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: Jul 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Early Classification of Bladder Cancer Using Spectrum-Aided Visual Enhancer (SAVE) and Deep Learning Models: A

Min-Hsin Yang1,2, Yaswanth Nagisetti3, Arvind Mukundan4,5

  • 1Institute of Medicine, Chung Shan Medical University, 402 No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan.

Cancers
|July 15, 2026
PubMed

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

SAVE: Spectrum-Aided Visual Enhancement for AI-Based Skin Cancer Detection.

Diagnostics (Basel, Switzerland)·2026
Same author

Efficacy of Spectral-Aided Visual Enhancer in Classification of Esophageal Cancer.

Cancers·2026
Same author

Eco-Friendly Copper Oxide Nanoparticles Incorporated Polyvinylidene Difluoride Electrospun Nanofibers as Wearable Piezoelectric Nanogenerator.

Polymers·2026
Same author

Dementia Detection via Retinal Hyperspectral Imaging and Deep Learning: Clinical Dataset Analysis and Comparative Evaluation of Multiple Architectures.

Bioengineering (Basel, Switzerland)·2025
Same author

Artificial Intelligence-Assisted Dermatologic Screening: Epidemiology and Clinical Features of Basal Cell Carcinoma, Squamous Cell Carcinoma, Seborrheic Keratosis and Actinic Keratosis.

Bioengineering (Basel, Switzerland)·2025
Same author

Visible-Light Hyperspectral Reconstruction and PCA-Based Feature Extraction for Malignant Pleural Effusion Cytology.

Biosensors·2025
Summary

A new software-based diagnostic tool, Spectrum-Aided Vision Enhancer (SAVE), shows promise for improving bladder cancer detection. This computer-aided diagnostic system enhances early-stage cancer identification without needing costly equipment.

Area of Science:

  • Urology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Bladder cancer (BC) presents a growing global health challenge.
  • Current diagnostic methods like cystoscopy have limitations in early detection and staging.
  • There is a need for accessible and accurate bladder cancer diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a novel, software-driven computer-aided diagnostic (CAD) system for bladder cancer.
  • To assess the efficacy of the Spectrum-Aided Vision Enhancer (SAVE) in improving diagnostic accuracy, particularly for early-stage lesions.
  • To determine if SAVE can enhance diagnostic capabilities in resource-constrained settings.

Main Methods:

  • Development of a purely software-driven CAD system named Spectrum-Aided Vision Enhancer (SAVE).
Keywords:
GoogLeNetInceptionResNet V2Resnet 34Spectrum-Aided Vision Enhancer (SAVE)VGG16YOLOv5bladder cancercomputer-aided diagnostic (CAD)

Related Experiment Videos

Last Updated: Jul 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

  • Integration of deep learning algorithms with the SAVE system.
  • Comparative analysis of SAVE's performance against standard white light imaging (WLI) in medical imaging.

    • Main Results:

    • SAVE demonstrated comparable overall performance to standard WLI (p=0.41), indicating non-inferiority.
    • SAVE significantly improved F1-scores for challenging early-stage bladder cancer categories.
    • Specifically, the F1-score for the 'Above T1' class increased from 65% to 85% using VGG16 with SAVE.

    Conclusions:

    • SAVE offers a reliable baseline for bladder cancer detection.
    • The system enhances visual cues for complex lesions without expensive optical hardware.
    • SAVE provides advanced diagnostic capabilities for resource-limited clinical settings, promoting decentralized urological care.