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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Melanoma Thickness Prediction Based on Convolutional Neural Network With VGG-19 Model Transfer Learning
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Melanoma Thickness Prediction Based on Convolutional Neural Network With VGG-19 Model Transfer Learning

机译：基于VGG-19模型转移学习的基于卷积神经网络的黑色素瘤厚度预测

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摘要

Over the past two decades, malignant melanoma incidence rate has dramatically risen but melanoma mortality has only recently stabilized. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Thickness is one of the most important factor in melanoma prognosis and it is used to establish the size of the surgical margin, as well as to select patients for sentinel lymph node biopsy. However, little work has concentrated on the evaluation of melanoma thickness both from the clinical as well as computer-aided diagnostic side. To address this problem, we propose an effective computer-vision based machine learning tool that can perform the preoperative evaluation of melanoma thickness. The novelty of our approach is that we directly predict the thickness of the skin lesion into one of three classes: less than 0.75 mm, 0.76-1.5 mm, and greater that 1.5 mm. In this study, we use transfer learning of the pre-trained, adapted to our application VGG-19 convolutional neural network (CNN) with an adjusted densely-connected classifier. Due to the limited data we investigate the transfer learning method where we apply knowledge from model trained on a different task. Our database contains 244 dermoscopy images. Experiments confirm the developed algorithm's ability to classify skin lesion thickness with 87.2% overall accuracy what is a state-of-the-art result in melanoma thickness prediction.
机译：在过去的二十年中，恶性黑素瘤发生率显着上升，但黑色素瘤死亡率最近稳定。由于其对大多数晚期疾病患者的转移和缺乏有效疗法的倾向，早期发现黑素瘤是一种临床迫切。厚度是黑素瘤预后最重要的因素之一，它用于建立手术边缘的尺寸，以及为患者选择淋巴结活检。然而，很少的作品集中在从临床和计算机辅助诊断侧的对黑色素瘤厚度的评估。为了解决这个问题，我们提出了一种有效的基于计算机视觉的机器学习工具，可以执行黑色素瘤厚度的术前评估。我们的方法的新颖性是，我们直接将皮肤病变的厚度预测成三类中的一个：小于0.75毫米，0.76-1.5毫米，更大的1.5毫米。在这项研究中，我们使用转移学习预先培训，适用于我们的应用程序VGG-19卷积神经网络（CNN），具有调整后的密集连接的分类器。由于数据有限，我们研究了从不同任务培训的模型应用知识的转移学习方法。我们的数据库包含244个Dermoscopy图像。实验证实了发达的算法对皮肤病变厚度的能力，总体精度为87.2％，是最先进的黑色素瘤厚度预测。

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