Jove
Visualize
Contact Us

Related Concept Videos

Skin Cancer01:30

Skin Cancer

6.7K
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...
6.7K
Pigmentation01:19

Pigmentation

5.1K
The color of the skin is influenced by a number of pigments, including melanin, carotene, and hemoglobin. Recall that melanin is produced by cells called melanocytes, which are found scattered throughout the stratum basale of the epidermis. The melanin is transferred to the keratinocytes via melanosomes.
Melanin occurs in two primary forms: eumelanin that provides black and brown pigment and pheomelanin that provides red color. Dark-skinned individuals produce more melanin than those with pale...
5.1K
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

31.1K
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,...
31.1K
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

16.0K
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...
16.0K
Classification of Illness01:17

Classification of Illness

9.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
9.5K
Classification of Systems-I01:26

Classification of Systems-I

712
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
712

You might also read

Related Articles

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

Sort by
Same author

Multiple Criteria Decision Analysis (MCDA) for evaluating cancer treatments in hospital-based health technology assessment: The Paraconsistent Value Framework.

PloS one·2022
Same author

A Heuristic and Data Mining Model for Predicting Broiler House Environment Suitability.

Animals : an open access journal from MDPI·2021
Same author

Paraconsistent Annotated Logic Algorithms Applied in Management and Control of Communication Network Routes.

Sensors (Basel, Switzerland)·2021
Same author

Validation of CD4<sup>+</sup> T-cell and viral load data from the HIV-Brazil Cohort Study using secondary system data.

BMC infectious diseases·2018
Same author

Paraconsistent artificial neural networks and Alzheimer disease: a preliminary study.

Dementia & neuropsychologia·2017
Same author

[Multi-criteria decision analysis for health technology resource allocation and assessment: so far and so near?]

Cadernos de saude publica·2017
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 Experiment Video

Updated: Apr 19, 2026

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells
06:09

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells

Published on: June 7, 2019

9.7K

Nevus and melanoma paraconsistent classification.

Sheila Souza1, Jair Minoro Abe1

  • 1Institute for Advanced Studies, University of São Paulo, Brazil.

Studies in Health Technology and Informatics
|December 10, 2014
PubMed
Summary

This study introduces Paraconsistent Artificial Neural Networks (PANN) for classifying skin conditions like Nevus and Melanoma. The novel approach effectively uses border features for accurate skin cancer detection.

More Related Videos

A 3D Organotypic Melanoma Spheroid Skin Model
08:49

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

16.9K
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
07:41

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

3.0K

Related Experiment Videos

Last Updated: Apr 19, 2026

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells
06:09

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells

Published on: June 7, 2019

9.7K
A 3D Organotypic Melanoma Spheroid Skin Model
08:49

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

16.9K
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
07:41

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

3.0K

Area of Science:

  • Computational intelligence
  • Dermatology
  • Medical imaging analysis

Background:

  • Nevus and Melanoma are common skin conditions, with Melanoma being a dangerous cancer.
  • Accurate classification of skin lesions is crucial for timely treatment.
  • Existing methods may struggle with imprecise or conflicting data in medical images.

Purpose of the Study:

  • To present the first study utilizing Paraconsistent Artificial Neural Networks (PANN) for Nevus and Melanoma classification.
  • To develop an automated process for classifying skin lesion images.
  • To evaluate the efficacy of PANN in handling imprecise and conflicting data for dermatological diagnosis.

Main Methods:

  • Implementation of a novel methodology based on Paraconsistent Artificial Neural Networks (PANN).
  • Development of an automated classification system for medical images.
  • Focusing on the analysis of border features for classification tasks.

Main Results:

  • The proposed PANN-based methodology achieved promising results in classifying Nevus and Melanoma.
  • The system demonstrated effectiveness in handling conflicting, paracomplete, and imprecise data.
  • Classification accuracy was notable when considering only border features.

Conclusions:

  • Paraconsistent Artificial Neural Networks (PANN) show significant potential for Nevus and Melanoma classification.
  • The developed automated process offers a promising approach for skin cancer detection.
  • Utilizing border features with PANN is an effective strategy for dermatological image analysis.