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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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Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation.

Shengxin Tao¹, Yun Jiang¹, Simin Cao¹

¹College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

This study introduces a new deep learning model for segmenting skin lesions, improving diagnostic accuracy. The attention-guided network with densely connected convolution enhances lesion boundary detection, aiding patient survival rates.

Keywords:

attention mechanism computer-aided diagnosis deep convolutional neural network skin lesion segmentation

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

Area of Science:

Medical image analysis
Computer vision
Dermatology

Background:

Accurate skin lesion segmentation is crucial for diagnosis and treatment, impacting patient survival rates.
Challenges include low contrast and indistinct boundaries, hindering precise segmentation.
Existing methods struggle with complex lesion characteristics.

Purpose of the Study:

To develop an advanced deep learning model for accurate skin lesion segmentation.
To address the challenges posed by low contrast and ambiguous boundaries in medical images.
To improve the overall performance and robustness of automated skin lesion analysis.

Main Methods:

Proposed a novel attention-guided network with densely connected convolution (CSAG and DCCNet).
Integrated a Channel Spatial Fast Attention-guided Filter (CSFAG) module into the network's skip connections.
Utilized densely connected convolution in the encoding path to enhance feature extraction.

Main Results:

Ablation experiments on the ISIC-2017 dataset validated the effectiveness of the CSFAG module and densely connected convolution.
The CSAG and DCCNet model achieved competitive segmentation performance compared to state-of-the-art algorithms.
Cross-dataset validation on the PH2 dataset demonstrated the model's robustness and generalizability.

Conclusions:

The proposed CSAG and DCCNet model, incorporating CSFAG and densely connected convolution, significantly improves skin lesion segmentation accuracy.
The attention-guided approach effectively handles low contrast and boundary challenges.
This method shows strong potential for clinical application in dermatology and improving patient outcomes.