A comparative study of deep learning architectures on melanoma detection
A comparative study of deep learning architectures on melanoma detection
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DOI：
10.1016/j.tice.2019.04.009
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发表时间：
2019-06-01
期刊：
影响因子：
2.6
通讯作者：
Kassani, Peyman Hosseinzadeh
中科院分区：
文献类型：
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作者：
Kassani, Sara Hosseinzadeh;Kassani, Peyman Hosseinzadeh
黑色素瘤是最具侵袭性的皮肤癌类型，它显著降低了预期寿命。早期发现黑色素瘤可以降低与皮肤癌相关的发病率和死亡率。由皮肤镜仪器获取的皮肤镜图像用于皮肤癌检测的计算分析。然而，一些图像质量的限制，如噪声，阴影，伪影存在，可能会损害皮肤图像分析的鲁棒性。因此，开发一种具有准确检测率的皮肤癌自动智能诊断系统至关重要。在本文中，我们评估了几种最先进的卷积神经网络在皮肤病变皮肤镜图像中的性能。我们的实验是在图形处理单元（GPU）上进行的，以加快训练和部署过程。为了提高图像的质量，我们采用了不同的预处理步骤。我们还应用数据增强方法，如水平和垂直翻转技术，以解决类偏度问题。预处理和数据扩充都有助于提高最终的准确性。
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However, some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing unit (GPU) to speed up the training and deployment process. To enhance the quality of images, we employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation could help to improve the final accuracy.