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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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A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

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New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.

Dan Popescu¹, Mohamed El-Khatib¹, Hassan El-Khatib¹

¹Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

Early detection of melanoma, a serious skin cancer, is crucial. This review explores artificial intelligence, particularly neural networks, for accurate melanoma diagnosis, aiding dermatologists and improving patient outcomes.

Keywords:

deep learning image classifiers image processing image segmentation machine learning melanoma detection neural networks review skin lesion statistic performances

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Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Melanoma incidence and mortality rates necessitate early detection.
Computer-aided diagnosis systems are vital for assisting dermatologists.
Artificial intelligence (AI) shows promise in enhancing melanoma detection accuracy.

Purpose of the Study:

To systematically review recent advances in AI-based melanoma detection.
To compare the effectiveness of various neural network architectures for melanoma diagnosis.
To identify current trends and future research directions in AI for melanoma detection.

Main Methods:

Systematic review of scientific literature from 2015-2021, with a focus on 2018-2021.
Analysis of neural network architectures, particularly those employing decision fusion.
Investigation of databases used for training melanoma detection models.

Main Results:

Identified key theoretical and applied contributions in neural network development for melanoma detection.
Highlighted the growing trend of using decision fusion in neural network architectures.
Summarized prevalent databases and methodologies for training AI models.

Conclusions:

AI, especially neural networks, offers significant potential as an intelligent support system for dermatologists in early melanoma detection.
Recent trends focus on advanced neural network architectures and decision fusion techniques.
Further research is needed to advance the field and address emerging trends in AI-driven melanoma diagnosis.