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<!DOCTYPE html>
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<head>
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<title>Skin Lesion Classification</title>
<meta name="description" content="Optimized Convolutional Neural Network Models For Skin Lesion Classification" />
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<section class="project-cs-hero">
<div class="project-cs-hero__content">
<h1 class="heading-primary">Optimized CNNs Models For Skin Lesion Classification</h1>
<div class="project-cs-hero__info">
<p class="text-primary">
Research project carried out with the aim of developing a Computer Aided Diagnosis (CAD) tool
to detect different skin lesions, using Transfer Learning and data augmentation.
</p>
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<div class="project-cs-hero__cta">
<a href="https://www.techscience.com/cmc/v70n2/44641" class="btn btn--bg" target="_blank">Paper Link</a>
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alt="Project Image"
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<div class="project-details__desc">
<h3 class="project-details__content-title">Project Overview</h3>
<p class="project-details__desc-para">
Skin cancer is one of the most severe diseases, and medical imaging
is among the main tools for cancer diagnosis. The images provide information
on the evolutionary stage, size, and location of tumor lesions. This paper
focuses on the classification of skin lesion images considering a framework
of four experiments to analyze the classification performance of Convolutional
Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based
on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment,
different workflow stages are tested, including data augmentation and fine-tuning optimization.
Three CNN models based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed
and compared using the HAM10000 dataset. The results obtained by the three models demonstrate
accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the
ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents
a helpful tool to accurately diagnose skin cancer disease.
</p>
</div>
<div class="project-details__desc">
<h3 class="project-details__content-title">Results Highlight</h3>
<p class="project-details__desc-para">
The results presented in this section correspond to the experiments performed in [1] using
transfer learning on the DenseNet-201, Inception-ResNet-V2, and Inception-V3 networks. Specifically, the
accuracies presented were achieved using Data Augmentation to level out the mismatch in the database
and prevent overtraining with the data. For more information, please refer to the scientific paper
article linked below.
</p>
<table>
<caption style="caption-side:bottom; text-align: center;">Accuracies for DenseNet-201, Inception-ResNet-V2, and Inception-V3 using Transfer Learning [1].</caption>
<tr>
<th>Model</th>
<th>Train</th>
<th>Validation</th>
<th>Test</th>
</tr>
<tr>
<td>DenseNet-201</td>
<td>1.00</td>
<td>0.98</td>
<td>0.98</td>
</tr>
<tr>
<td>Inception-ResNet-V2</td>
<td>0.99</td>
<td>0.96</td>
<td>0.97</td>
</tr>
<tr>
<td>Inception-V3</td>
<td>0.99</td>
<td>0.97</td>
<td>0.96</td>
</tr>
</table>
</div>
<div class="project-details__desc">
<h3 class="project-details__content-title">Products</h3>
<p class="project-details__desc-para">
[1] J. Villa-Pulgarin et al., “Optimized Convolutional Neural Network Models for Skin Lesion Classification,”
<i>Computers, Materials & Continua.</i>, vol. 70, pp. 2131-2148, Sep. 2021, doi:
<a style="color: #0062b9; text-decoration-line: underline;" href="https://doi.org/10.32604/cmc.2022.019529"
target="_blank" rel="noreferrer">10.32604/cmc.2022.019529</a>.
</p>
<div class="project-details__tools-used">
<h3 class="project-details__content-title">Tools Used</h3>
<div class="skills">
<div class="skills__skill">Python</div>
<div class="skills__skill">TensorFlow</div>
<div class="skills__skill">Keras</div>
<div class="skills__skill">Data Augmentation</div>
</div>
</div>
</div>
<div class="project-details__links">
<h3 class="project-details__content-title">See Resources</h3>
<a
href="https://www.techscience.com/cmc/v70n2/44641"
class="btn btn--med btn--theme project-details__links-btn"
target="_blank"
>Paper Link</a
>
<a
href="https://github.com/BioAITeam/Classification-of-skin-lesions-using-CNN-TransferLearning-and-Data-Augmentation"
class="btn btn--med btn--theme-inv project-details__links-btn"
target="_blank"
>Code Link</a
>
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