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

Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review.

Bioengineering (Basel, Switzerland)·2025

Same author

Mathematical Modeling and Control of COVID-19 Using Super Twisting Sliding Mode and Nonlinear Techniques.

Computational intelligence and neuroscience·2022

Same author

Identification of Candidate Immune System MicroRNAs Differentially Found in Colostrum and Milk Exosomes.

MicroRNA (Shariqah, United Arab Emirates)·2022

Same author

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images.

Cancers·2022

Same author

Identification of novel QTLs for grain fertility and associated traits to decipher poor grain filling of basal spikelets in dense panicle rice.

Scientific reports·2021

Same author

Comparative transcriptome profiling of low light tolerant and sensitive rice varieties induced by low light stress at active tillering stage.

Scientific reports·2019

Search research articles

Related Experiment Video

Updated: Aug 20, 2025

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

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification.

Niharika Mohanty¹, Manaswini Pradhan¹, Annapareddy V N Reddy²

¹Department of Information and Communication Technology, Fakir Mohan University, Balasore 756089, India.

|November 26, 2022

Summary

This summary is machine-generated.

This study introduces novel feature selection strategies for skin cancer classification using deep learning models and an artificial jellyfish algorithm. The Feature-based Optimized Weighted Feature Set (FOWFS-AJS) strategy achieved the highest accuracy on skin lesion datasets.

Keywords:

BCN 20000 dataset EfficientNet B0 HAM 10000 dataset ResNet50 VGG16 feature selection skin lesion classification

More Related Videos

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Aug 20, 2025

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

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Dermatology and Computational Biology
Machine Learning in Medical Imaging

Background:

Accurate skin cancer classification relies on effective feature selection from complex datasets like HAM10000 and BCN20000.
Pre-trained Convolutional Neural Network (CNN) models offer powerful feature extraction capabilities but require optimization for specific tasks.

Purpose of the Study:

To develop and evaluate advanced feature selection strategies for enhancing skin cancer classification accuracy.
To compare the performance of proposed strategies against established methods using multiple classifiers and metrics.

Main Methods:

Proposed three feature fusion strategies: Adaptive Weighted Feature Set (AWFS), Model-based Optimized Weighted Feature Set (MOWFS), and Feature-based Optimized Weighted Feature Set (FOWFS).
Utilized pre-trained CNN models (VGG16, EfficientNet B0, ResNet50) for feature extraction.
Employed the artificial jellyfish (AJS) algorithm for optimizing feature weights in MOWFS and FOWFS strategies.
Evaluated feature selection performance using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers.

Main Results:

The FOWFS-AJS strategy demonstrated superior performance, achieving the highest accuracy (94.05% on HAM10000, 94.90% on BCN20000) when combined with the SVM classifier.
Performance was assessed using accuracy, precision, sensitivity, F1-score, and Area Under the Receiver Operating Characteristic Curves (AUC-ROC).
Statistical analysis using the Friedman test confirmed the effectiveness of the proposed strategies, with FOWFS-AJS showing significant advantages.

Conclusions:

The FOWFS-AJS strategy, leveraging the meta-heuristic AJS algorithm, provides an effective approach for optimal feature selection in skin lesion classification.
The study highlights the potential of combining deep learning feature extraction with advanced optimization algorithms for improved diagnostic accuracy in dermatology.