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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

Yuexiang Li^1,2, Linlin Shen^3,4

¹Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. yuexiang.li@szu.edu.cn.

Sensors (Basel, Switzerland)

|February 15, 2018

Summary

This study introduces two deep learning methods for accurate melanoma detection from dermoscopy images, improving skin lesion segmentation, feature extraction, and classification for better early diagnosis.

Keywords:

deep convolutional network fully-convolutional residual network melanoma recognition skin lesion classification

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Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Skin lesions, particularly melanoma, pose a global health challenge.
Early melanoma detection via dermoscopy is crucial for survival rates.
Accurate melanoma recognition is difficult due to low contrast and visual similarities.

Purpose of the Study:

To develop reliable automatic detection of skin tumors using deep learning.
To enhance accuracy and efficiency in dermatopathology.
To address lesion segmentation, feature extraction, and classification tasks.

Main Methods:

Proposed two deep learning frameworks: a fully convolutional residual network (FCRN) pair for segmentation and coarse classification.
Introduced a lesion index calculation unit (LICU) for refining classification using distance heat-maps.
Utilized a convolutional neural network (CNN) for dermoscopic feature extraction.

Main Results:

Achieved an accuracy of 0.753 for lesion segmentation (task 1).
Obtained an accuracy of 0.848 for dermoscopic feature extraction (task 2).
Reached an accuracy of 0.912 for lesion classification (task 3) on the ISIC 2017 dataset.

Conclusions:

The proposed deep learning frameworks demonstrate promising performance for skin lesion analysis.
These methods can significantly aid pathologists in accurate and efficient melanoma diagnosis.
The study highlights the potential of AI in improving skin cancer detection rates.