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 Concept Videos

Skin Cancer01:30

Skin Cancer

5.7K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
5.7K
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

19.6K
Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
19.6K
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

12.5K
Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Meta-learners for few-shot weakly-supervised optic disc and cup segmentation on fundus images.

Computers in biology and medicine·2025
Same author

Author Correction: A streamlined approach for intelligent ship object detection using EL-YOLO algorithm.

Scientific reports·2024
Same author

Glioma Grade and Molecular Markers: Comparing Machine-Learning Approaches Using VASARI (Visually AcceSAble Rembrandt Images) Radiological Assessment.

Cureus·2024
Same author

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm.

Scientific reports·2024
Same author

Boosting the performance of pretrained CNN architecture on dermoscopic pigmented skin lesion classification.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2023
Same author

Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural Network Method.

Journal of imaging·2023

Related Experiment Video

Updated: Jan 17, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.8K

Enhancing dermoscopic pigmented skin lesion classification: A refined approach using the pre-trained Inception-V3

Erwin S Nugroho1,2, Igi Ardiyanto1, Hanung A Nugroho1

  • 1Departement of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Narra J
|September 15, 2025
PubMed
Summary

This study developed an advanced AI model for classifying skin lesions from dermoscopy images, significantly improving diagnostic accuracy and aiding early skin cancer detection. The AI framework enhances consistency and accessibility in diagnosing pigmented skin lesions.

Keywords:
Inception-V3Medical image processingconvolutional neural networkdermoscopypigmented skin lesion

More Related Videos

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

533
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.2K

Related Experiment Videos

Last Updated: Jan 17, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.8K
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

533
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.2K

Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Imaging Analysis
  • Computational Pathology

Background:

  • Skin cancer diagnosis relies heavily on expert interpretation of dermoscopy images, leading to variability and accessibility issues.
  • Automated solutions are needed to improve diagnostic consistency and facilitate early detection of pigmented skin lesions.
  • Current diagnostic methods face challenges in resource-limited settings.

Purpose of the Study:

  • To develop a refined machine learning framework for classifying pigmented skin lesions using dermoscopy images.
  • To enhance the accuracy and reliability of automated skin lesion classification.
  • To provide a tool that aids dermatologists in early skin cancer detection.

Main Methods:

  • Utilized an enhanced Inception-V3 convolutional neural network model.
  • Integrated a simplified soft-attention mechanism and advanced data augmentation.
  • Employed Bayesian hyperparameter tuning and ImageNet transfer learning on the ISIC-2019 dataset.
  • Implemented preprocessing steps including resizing, cleaning, and data balancing.

Main Results:

  • Achieved high performance metrics on the ISIC-2019 dataset: 98.5% sensitivity, 99.62% specificity, 97.42% precision, 97.38% accuracy, 97.34% F1 score, and 0.99 AUC.
  • The soft-attention mechanism improved the model's ability to identify relevant lesion features.
  • Demonstrated superior capability in accurate and reliable classification of pigmented skin lesions.

Conclusions:

  • The developed machine learning framework offers a significant advancement in automated skin lesion classification.
  • The model's high performance indicates its potential to improve early skin cancer diagnosis and accessibility.
  • This AI-driven approach surpasses current benchmarks, offering a reliable tool for dermatological applications.