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Related Experiment Video

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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Skin Cancer Detection Using Deep Learning-A Review.

Maryam Naqvi¹, Syed Qasim Gilani², Tehreem Syed³

¹Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea.

Diagnostics (Basel, Switzerland)

|June 10, 2023

Summary

This summary is machine-generated.

Early skin cancer detection using deep learning improves diagnosis accuracy and survival rates. This review highlights recent advancements in deep learning models and datasets for effective skin cancer classification.

Keywords:

classification deep learning segmentation skin cancer

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Skin cancer is a significant global health concern and a leading cause of cancer-related deaths worldwide.
Current diagnostic methods, primarily visual inspection, suffer from limitations in accuracy, underscoring the need for improved detection strategies.
Early diagnosis is critical for reducing mortality rates associated with skin cancer.

Purpose of the Study:

To survey recent advancements in deep learning methodologies for skin cancer classification.
To provide an overview of commonly utilized deep learning models in dermatological research.
To identify and discuss prevalent datasets employed in the training and validation of skin cancer classification algorithms.

Main Methods:

Systematic review of recent research articles focusing on deep learning for skin cancer classification.
Analysis of commonly used deep learning architectures (e.g., Convolutional Neural Networks).
Examination of publicly available and proprietary datasets used in skin cancer image analysis.

Main Results:

Deep learning models demonstrate significant potential in enhancing the accuracy of skin cancer diagnosis compared to traditional methods.
Convolutional Neural Networks are frequently employed and show high performance in classifying various skin lesion types.
The availability and quality of diverse datasets are crucial for the generalization and robustness of deep learning models.

Conclusions:

Deep learning offers a promising avenue for improving early and accurate skin cancer diagnosis.
Further research and development in deep learning models and curated datasets are essential for clinical translation.
AI-assisted diagnostic tools can support dermatologists, potentially leading to better patient outcomes and reduced healthcare costs.