Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Comput Biol Med. 2024 Dec;183:109250. doi: 10.1016/j.compbiomed.2024.109250. Epub 2024 Oct 12.
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a novel feature: the number of colors present in lesions, which can indicate the severity of skin diseases and help distinguish melanomas from benign lesions. We propose a color histogram analysis, a traditional image processing technique, to analyze the pixels of skin lesions from three publicly available datasets: PH2, ISIC2016, and Med-Node, which include dermoscopic and non-dermoscopic images. While the PH2 dataset contains ground truth about skin lesion colors, the ISIC2016 and Med-Node datasets lack such annotations; our algorithm establishes this ground truth using the color histogram analysis based on the PH2 dataset. We then design and train a 19-layer Convolutional Neural Network (CNN) with different skip connections of residual blocks to classify lesions into three categories based on the number of colors present. The DeepDream algorithm is utilized to visualize the learned features of different layers, and multiple configurations of the proposed CNN are tested, achieving the highest weighted F1-score of 75.00 % on the test set. LIME is subsequently applied to identify the most important features influencing the model's decision-making. The findings demonstrate that the number of colors in lesions is a significant feature for describing skin conditions. The proposed CNN, particularly with three skip connections, shows strong potential for clinical application in diagnosing melanoma, supporting its use alongside traditional diagnostic methods.
皮肤损伤的颜色是鉴别恶性黑色素瘤和其他皮肤疾病的重要诊断特征。与黑色素细胞病变相关的典型颜色包括棕褐色、黑色、红色、白色和蓝灰色。本研究引入了一个新的特征：病变中存在的颜色数量，这可以指示皮肤疾病的严重程度，并有助于区分黑色素瘤和良性病变。我们提出了一种颜色直方图分析方法，这是一种传统的图像处理技术，用于分析来自三个公开数据集（PH2、ISIC2016 和 Med-Node）的皮肤损伤像素，这些数据集包括皮肤镜和非皮肤镜图像。虽然 PH2 数据集包含皮肤损伤颜色的真实信息，但 ISIC2016 和 Med-Node 数据集缺乏此类注释；我们的算法使用基于 PH2 数据集的颜色直方图分析来建立此真实信息。然后，我们设计并训练了一个具有不同残差块跳过连接的 19 层卷积神经网络（CNN），根据存在的颜色数量将病变分为三类。使用 DeepDream 算法可视化不同层的学习特征，并测试了所提出的 CNN 的多种配置，在测试集上获得了最高加权 F1 分数 75.00%。随后，应用 LIME 来识别影响模型决策的最重要特征。研究结果表明，病变中的颜色数量是描述皮肤状况的重要特征。所提出的 CNN，特别是具有三个跳过连接的 CNN，在诊断黑色素瘤方面具有很强的临床应用潜力，支持其与传统诊断方法一起使用。
