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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: Sep 17, 2025

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

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Vida Esmaeili¹, Mahmood Mohassel Feghhi², Hadi Seyedarabi¹

¹Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 51666, Iran.

Scientific Reports

|July 2, 2025

Summary

This study introduces novel feature extraction methods and an optimized four-stream convolutional neural network (CNN) for accurate melanoma detection. The advanced approach achieves high accuracy in identifying malignant skin cancer from dermoscopy images.

Keywords:

Classification Convolutional neural network Denoising Melanoma detection Skin cancer

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Area of Science:

Dermatology
Medical Imaging
Computer Science

Background:

Melanoma is a deadly skin cancer with increasing global incidence, necessitating accurate early diagnosis.
Automatic melanoma detection faces challenges including imbalanced datasets, lesion variability, and limitations of existing deep learning models in recognizing sample relationships.

Purpose of the Study:

To develop an advanced system for automatic melanoma detection in dermoscopy images.
To overcome limitations of current methods by proposing new feature extraction techniques and an optimized deep learning architecture.

Main Methods:

Pre-processing techniques including hair removal, generative adversarial network (GAN)-based balancing, CNN-based denoising, and image enhancement.
Four novel feature extraction methods: ULBP-CVA, multi-block ULBP-NP, multi-block GULBP-NP, and multi-block gradient ULBP-NP, utilizing nine proposed planes.
An optimized four-stream CNN (OFSCNN) for classifying lesion color, edges, texture, local-spatial frequency, and gradient features.

Main Results:

The proposed OFSCNN achieved high detection rates across multiple datasets: 99.8% (HAM 10000), 99.9% (ISIC 2024), 99.62% (ISIC 2017), and 99.6% (ISIC 2016).
The novel feature extraction methods effectively capture crucial spatial and local variations, enabling differentiation of similar lesions.
Simulation results demonstrate superior performance compared to state-of-the-art methods for melanoma detection.

Conclusions:

The proposed method offers a promising solution for accurate and automatic melanoma detection.
The combination of advanced pre-processing, novel feature extraction, and the OFSCNN architecture significantly improves diagnostic performance.