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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 29, 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

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.

Mohammed A Al-Masni¹, Dong-Hyun Kim¹, Tae-Seong Kim²

¹Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.

Computer Methods and Programs in Biomedicine

|February 7, 2020

Summary

This study introduces a deep learning framework for automated skin lesion diagnosis, improving classification accuracy by segmenting lesions first. The integrated system aids dermatologists in diagnosing skin cancer more effectively.

Keywords:

CAD CNN Classification Deep learning ISIC Melanoma Segmentation Skin lesion

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Published on: August 8, 2025

Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare
Computational Pathology

Background:

Automated diagnosis of skin lesions from dermoscopy images is challenging.
Accurate feature extraction is crucial for reliable skin lesion classification.

Purpose of the Study:

To develop an integrated deep learning framework for automated skin lesion diagnosis.
To enhance classification performance through a two-stage approach: segmentation and classification.

Main Methods:

A deep learning framework combining skin lesion boundary segmentation using FrCN and classification using Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201.
Evaluation on three independent datasets (ISIC 2016, 2017, 2018) with data balancing, segmentation, and augmentation.

Main Results:

The integrated system improved Inception-ResNet-v2's F1-score for benign and malignant cases by up to 4.71% on the ISIC 2016 dataset.
ResNet-50 achieved superior weighted prediction accuracies across datasets: 79.95% (ISIC 2016), 81.57% (ISIC 2017), and 89.28% (ISIC 2018).

Conclusions:

The proposed integrated diagnostic networks can assist dermatologists in improving skin cancer diagnosis.
Deep learning-based segmentation and classification offer a promising approach for automated dermatological diagnostics.