Search research articles

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

PROTEAN: An Explainable AI Pipeline for Protein Classification in Support of Precision Medicine.

IEEE journal of biomedical and health informatics·2026

Same author

Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic Radiographs.

Orthodontics & craniofacial research·2026

Same author

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem.

BMC bioinformatics·2023

Same author

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier.

BMC bioinformatics·2022

Same author

YAMACS: a graphical interface for GROMACS.

Bioinformatics (Oxford, England)·2022

Same author

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques.

PloS one·2022

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: Oct 19, 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

A Cloud Approach for Melanoma Detection Based on Deep Learning Networks.

Luigi Di Biasi, Alessia Auriemma Citarella, Michele Risi

IEEE Journal of Biomedical and Health Informatics

|September 20, 2021

Summary

This summary is machine-generated.

This study explores melanoma detection using deep learning, emphasizing dataset parameter tuning and a hybrid cloud-fog-edge architecture for efficient, robust image analysis and early diagnosis.

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

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

Related Experiment Videos

Last Updated: Oct 19, 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

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

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

Area of Science:

Computer Vision
Machine Learning
Deep Learning in Dermatology

Background:

Digitized medical images offer vast potential for knowledge extraction and early disease diagnosis.
Deep neural networks are increasingly utilized in dermatology for distinguishing between melanoma and non-melanoma skin lesions.

Purpose of the Study:

To investigate the impact of dataset parameter modifications on classifier accuracy in melanoma detection.
To propose a flexible, hybrid Cloud-Fog-Edge computing architecture for a robust Melanoma Detection service.
To reduce the computational time for continuous retraining of models handling large datasets.

Main Methods:

Investigated Transfer Learning issues by analyzing the effect of dataset parameter changes on classifier accuracy.
Developed and implemented a hybrid Cloud, Fog, and Edge Computing architecture for melanoma detection.
Conducted experiments comparing single-machine processing with distributed systems to evaluate retraining efficiency.

Main Results:

Simple dataset parameter changes significantly alter classifier accuracy, highlighting the need for continuous training-test iterations.
The proposed hybrid architecture effectively handles large data volumes and reduces retraining time.
Distributed computing approaches demonstrate significantly improved output achievement times compared to single-machine setups.

Conclusions:

Robust melanoma detection models require continuous training-test iterations and careful parameter tuning.
A hybrid Cloud-Fog-Edge architecture offers a flexible and efficient solution for real-time melanoma detection services.
Distributed systems are crucial for managing large-scale image analysis and accelerating model retraining in dermatology.