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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...

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Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Published on: November 25, 2011

Performance analysis of melanoma classifier using electrical modeling technique.

Tanusree Roy¹, Pranabesh Bhattacharjee²

¹Department of Electrical and Electronics Engineering, University of Engineering and Management, Kolkata, 700135, India. tanusree.rinki@gmail.com.

Medical & Biological Engineering & Computing

|August 10, 2020

Summary

This summary is machine-generated.

A novel electrical model identifies melanoma-related genes using amino acid properties. This approach achieves 94% accuracy and 96% sensitivity for early skin cancer detection.

Keywords:

Electrical modeling Gene Real-time classifier Simulation Skin cancer

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Area of Science:

Biophysics
Bioinformatics
Computational Biology

Background:

Melanoma skin cancer diagnosis relies on identifying specific genetic markers.
Traditional methods for gene identification can be time-consuming and lack real-time diagnostic capabilities.
Developing efficient computational models is crucial for advancing melanoma research.

Purpose of the Study:

To propose a novel and efficient modeling approach for identifying melanoma-related genes.
To develop an equivalent electrical model for designing a melanoma classifier.
To implement and validate the proposed model for real-time skin cancer diagnosis.

Main Methods:

Modeling amino acids using RC passive circuits based on physicochemical structure and hydropathy.
Developing gene structure models from amino acid electrical models.
Implementing classifiers using NI LabVIEW-based hardware for real-time analysis.
Analyzing phase responses, pole-zero diagrams, and transient responses for gene screening.
Utilizing a color code scheme for enhanced gene analysis.

Main Results:

The proposed classifier achieved 94% classification accuracy.
The classifier demonstrated 96% sensitivity in identifying melanoma-related genes.
The model showed superiority compared to traditional diagnostic methods.
Real-time response observation was enabled through the LabVIEW-based hardware kit.

Conclusions:

The developed equivalent electrical model provides an efficient method for melanoma gene identification.
The novel approach offers a promising tool for early and accurate melanoma skin cancer diagnosis.
Integration with hardware allows for practical, real-time screening of genetic markers.