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Deep neural network-based robust framework for automated skin lesion segmentation and analysis
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DOI：10.1177/20552076261427501.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Skin lesion segmentation plays a critical role in computer-aided diagnosis systems, serving as a foundation for the early detection and treatment of skin cancer. Nonetheless, obtaining accurate segmentation remains difficult because of inconsistencies in lesion visual features, texture, image sharpness, and the presence of indistinct edges.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Objective</jats:title>
<jats:p>To develop and evaluate a novel deep neural network (DNN)-based approach for robust and accurate segmentation of skin lesions from dermoscopic images using advanced pre-processing and post-processing techniques.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>The proposed method integrates a DNN architecture with specialized pre-processing and post-processing modules. The pre-processing step enhances image quality by denoising and normalizing the lesion intensities. The DNN framework extracts hierarchical features, while the post-processing module refines segmentation masks by correcting boundary irregularities and removing artifacts. The model was tested using three widely recognized dermoscopic International Skin Imaging Collaboration (ISIC) image databases from the years 2016, 2017, and 2018 without extensive data augmentation. Statistical analysis, including the Wilcoxon signed-rank test, was conducted to compare performance with existing methods.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>
The proposed method achieved Jaccard index scores of
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>89.91</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.099</mml:mn>
</mml:math>
</jats:inline-formula>
(ISIC 2016),
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>84.51</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.135</mml:mn>
</mml:math>
</jats:inline-formula>
(ISIC 2017), and
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>87.39</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.139</mml:mn>
</mml:math>
</jats:inline-formula>
(ISIC 2018), and Dice coefficients of
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>94.30</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.076</mml:mn>
</mml:math>
</jats:inline-formula>
,
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>90.86</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.104</mml:mn>
</mml:math>
</jats:inline-formula>
, and
<jats:inline-formula>
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll">
<mml:mn>92.53</mml:mn>
<mml:mo>±</mml:mo>
<mml:mn>0.102</mml:mn>
</mml:math>
</jats:inline-formula>
, respectively. These results outperformed state-of-the-art methods such as U-shaped Convolutional Neural Network (U-Net), nested U-Net with dense skip connections (UNet++), and Swin-Unet in segmentation accuracy, consistency, and computational efficiency.
</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>This study presents a high-performing, scalable solution for automated skin lesion segmentation. The proposed method effectively addresses critical challenges by integrating robust feature extraction and boundary refinement, making it well-suited for real-world clinical applications in skin cancer diagnosis and management.</jats:p>
</jats:sec>
Keywords:
skin lesion segmentation
deep neural network
dermoscopic images
boundary refinement
skin cancer diagnosis
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