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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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Updated: Jun 27, 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

Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning.

Emil Andersson¹, Jenny Hult², Carl Troein¹

¹Centre for Environmental and Climate Science, Lund University, Lund, Sweden.

|April 29, 2024

Summary

This summary is machine-generated.

This study introduces a novel artificial intelligence (AI) approach for skin tumor delineation using hyperspectral imaging and artificial neural networks (ANNs). This method eliminates the need for ground truth images, offering a more clinically relevant diagnostic tool.

Keywords:

Computer science Health sciences Natural sciences

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Area of Science:

Medical imaging
Artificial intelligence in healthcare
Computational pathology

Background:

Digitalization of patient data is transforming healthcare.
AI and digital imaging show promise for diagnostics and decision-making.
Automatic pre-surgical skin tumor delineation is impactful but limited by current methods requiring ground truth images.

Purpose of the Study:

To develop a novel, clinically relevant approach for automatic pre-surgical skin tumor delineation.
To overcome the limitation of requiring ground truth images in current delineation methods.

Main Methods:

Utilizing hyperspectral images to obtain spectral data from healthy tissue and tumors.
Employing artificial neural networks (ANNs) to generate prediction maps from spectral data.
Applying a segmentation algorithm to automatically identify tumor borders based on ANN predictions.

Main Results:

The novel approach successfully delineates skin tumor borders without relying on ground truth images.
ANN models trained on individual patient data provide a clinically relevant approach.

Conclusions:

This AI-driven method using hyperspectral imaging and ANNs offers a viable alternative for pre-surgical skin tumor delineation.
The approach circumvents the need for clinically impractical ground truth images, enhancing diagnostic capabilities.