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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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Related Experiment Video

Updated: Dec 26, 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

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Kashan Zafar¹, Syed Omer Gilani¹, Asim Waris¹

¹Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)

|March 19, 2020

Summary

This summary is machine-generated.

This study introduces an automated Res-Unet model for precise skin lesion segmentation from dermoscopic images, improving early melanoma detection and clinical treatment accuracy.

Keywords:

Jaccard Index ROC curve ResNet U-Net convolutional neural networks dermoscopic images image inpainting melanoma

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare
Computational Pathology

Background:

Accurate skin lesion boundary delimitation is crucial for timely diagnosis and treatment of skin cancer, particularly aggressive melanoma.
Current segmentation methods using dermoscopic images often lack the required accuracy for effective clinical application.
Automated models offer a promising solution to enhance the precision and efficiency of skin lesion analysis.

Purpose of the Study:

To develop and validate an automated method for accurate segmentation of skin lesion boundaries from dermoscopic images.
To improve the localization of cancerous regions for better clinical treatment planning.
To address the limitations of existing methods in achieving high segmentation accuracy.

Main Methods:

Proposed an automated segmentation model combining U-Net and ResNet architectures, termed Res-Unet.
Utilized image inpainting technique for effective hair removal from dermoscopic images to enhance segmentation.
Trained and validated the model on the ISIC 2017 dataset and the PH² dataset.

Main Results:

The Res-Unet model achieved a Jaccard Index of 0.772 on the ISIC 2017 test set.
The model demonstrated a Jaccard Index of 0.854 on the PH² dataset.
Image inpainting significantly improved the overall segmentation performance.

Conclusions:

The proposed Res-Unet model provides accurate and automated segmentation of skin lesion boundaries.
The method shows comparable performance to state-of-the-art techniques, offering a viable tool for clinical settings.
Automated segmentation, enhanced by hair removal, holds significant potential for improving melanoma diagnosis and treatment.