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

4.2K
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...
4.2K
Classification of Leukocytes01:30

Classification of Leukocytes

2.1K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.1K
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

5.6K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.6K
Cancer Survival Analysis01:21

Cancer Survival Analysis

402
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
402
Skin Diseases and Disorders01:23

Skin Diseases and Disorders

3.9K
Skin is the first line of defense and encounters a variety of microbes. Some pathogenic strains are often the cause of a broad range of infections of the skin and other body systems. These conditions can affect people of all ages and may have different causes, including genetic factors, infections, autoimmune reactions, environmental factors, and lifestyle choices.
Gram-positive Staphylococcus spp. and Streptococcus spp. are responsible for many of the most common skin infections. However, many...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Cognitive-affective network structure in adolescents with non-suicidal self-injury: implications for clinical intervention.

Frontiers in psychiatry·2026
Same author

SlWRKY6 Mediates H2S Signaling and SlGRF1-SlGIF2 Module to Coordinately Regulate Plant Growth, Fruit yield and Ripening in Tomato.

Plant physiology·2026
Same author

Spatial heterogeneity of health care resources: a study of the socioeconomic, demographic, and natural geographic conditions of ethnic minority regions in Southwestern China.

BMC public health·2026
Same author

Static and dynamic brain region activation abnormalities in schizophrenia: Evidence from fNIRS.

Schizophrenia research·2026
Same author

Hierarchical Studies in TRAP2 Mice Demonstrate that Neuronal Activation and Mitochondrial Networks Integration Constitute the Key Mechanism Underlying Painful Syncope.

Neuroscience bulletin·2026
Same author

Development of Hexaploid Wheat Germplasm with Resistance to Both Powdery Mildew and Stripe Rust by Introgression of <i>Pm60</i> and <i>YrU1</i> from <i>Triticum urartu</i>.

Plants (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 25, 2025

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

2.4K

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for Classification of Skin

Nannan Liu1, M R Rejeesh2, Vinu Sundararaj2

  • 1School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, 315211, China.

Expert Systems with Applications
|June 26, 2023
PubMed
Summary

A new method, Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine (ACO-KSELM), improves skin cancer prediction accuracy using feature extraction on high-dimensional biomedical data. This approach achieves over 98% accuracy in classifying various skin cancer types.

Keywords:
Anti coronavirus optimizationHigh dimensional datasetKernel-based soft plus extreme learning machineprediction accuracyskin cancer

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
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

16.9K

Related Experiment Videos

Last Updated: Jul 25, 2025

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

2.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
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

16.9K

Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Dermatology

Background:

  • High-dimensional biomedical datasets often contain redundant features, hindering accurate disease diagnosis.
  • Effective feature extraction is crucial for identifying underlying data patterns and improving predictive model performance.

Purpose of the Study:

  • To introduce a novel method, Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine (ACO-KSELM), for accurate skin cancer prediction.
  • To address the challenges posed by large-dimensional datasets in disease diagnosis.

Main Methods:

  • Utilized four skin cancer image datasets (ISIC 2016, ACS, HAM10000, PAD-UFES-20).
  • Applied Gaussian filters for noise reduction and employed color histogram, Haralick texture, and Hu moment extraction for feature identification.
  • Implemented the proposed ACO-KSELM model for classification of skin cancer types.

Main Results:

  • Achieved high prediction accuracies: 98.9% (ISIC 2016), 98.7% (ACS), 98.6% (HAM10000), and 97.9% (PAD-UFES-20).
  • Successfully classified extracted features into Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen's disease (BOD), Melanoma (MEL), and Nevus (NEV).

Conclusions:

  • The ACO-KSELM method demonstrates significant potential for accurate skin cancer diagnosis from high-dimensional dermoscopic images.
  • The proposed feature extraction and classification approach effectively handles complex biomedical data for improved diagnostic accuracy.