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

Skin Cancer

Skin Cancer

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...

You might also read

Related Articles

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

Sort by

Same author

Senior populations' attitude towards virtual assistants: A study using extended technology acceptance model.

Assistive technology : the official journal of RESNA·2026

Same author

Deep-Learning-Based Classification of Lung Adenocarcinoma and Squamous Cell Carcinoma Using DNA Methylation Profiles: A Multi-Cohort Validation Study.

Cancers·2026

Same author

Edge Driven Trust Aware Threat Detection for IoT Enabled Intelligent Transportation Systems.

Sensors (Basel, Switzerland)·2026

Same author

ESA-YOLOv5m: a lightweight spatial and improved attention-driven detection for brain tumor MRI analysis.

Frontiers in medicine·2026

Same author

Explainable Cluster-Based Predictive Framework for Early Diagnosis of Autism Spectrum Disorder Using Behavioral Biomarkers.

Diagnostics (Basel, Switzerland)·2025

Same author

Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.

BioMed research international·2025

Search research articles

Related Experiment Video

Updated: Jun 25, 2025

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

Melanoma identification and classification model based on fine-tuned convolutional neural network.

Maram F Almufareh¹, Noshina Tariq², Mamoona Humayun¹

¹Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah Al Jouf, Saudi Arabia.

|May 27, 2024

Summary

This study introduces a deep learning model for early melanoma detection using AI. The Convolutional Neural Network (CNN) achieved improved accuracy in classifying skin lesions, aiding early diagnosis and patient outcomes.

Keywords:

Internet of medical things convolutional neural networks deep learning hyperparameter fine-tuning melanoma support vectormachine transfer learning

More Related Videos

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

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

Published on: August 18, 2022

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 25, 2025

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

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

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

Published on: August 18, 2022

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin cancer, particularly melanoma, is a lethal and unpredictable disease.
Advancements in Artificial Intelligence (AI) and image recognition are revolutionizing diagnostics.
Early detection is crucial for improving patient outcomes in skin cancer cases.

Purpose of the Study:

To develop a robust image classification model for the early detection of melanoma.
To support Internet of Medical Things (IoMT) applications through advanced diagnostic tools.
To leverage Deep Learning (DL) and Convolutional Neural Network (CNN) for enhanced melanoma diagnosis.

Main Methods:

Utilized a CNN-based approach for image classification of dermatoscopic images.
Analyzed publicly available datasets including DermIS, DermQuest, and ISIC2019.
Employed convolutional, pooling, and fully connected layers for feature extraction and classification, incorporating transfer learning.

Main Results:

The proposed CNN model demonstrated high accuracy in distinguishing between malignant and benign skin lesions.
Achieved improved detection accuracy compared to current best practices: 5% in DermIS, 6% in DermQuest, and 0.81% in ISIC2019.
Utilized Soft-max classification and support vector machines to assess deep feature classification performance.

Conclusions:

The study highlights the significant potential of CNNs in melanoma detection.
The developed model aids dermatologists in accurate decision-making for early diagnosis.
Contributes to improved patient outcomes through enhanced skin cancer diagnosis capabilities.