Melanoma Classification on Dermoscopy Skin Images using Bag Tree Ensemble Classifier

N. Lynn, Nu War
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引用次数: 5

Abstract

Melanoma classification on dermoscopy skin images is a demanding work as a result of the low contrast of the lesion images, the intra-structural variants of melanomas, the much visually likeliness level of whether melanoma or non-melanoma lesions, and the covering of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation process, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the extraction of features with the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, colored and textural features are computed from the segmented region. Lastly, the extracted features are classified to identify if the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset.
基于袋树集成分类器的皮肤镜皮肤图像黑色素瘤分类
由于病变图像对比度低、黑素瘤的结构内变异、黑素瘤或非黑素瘤病变的视觉相似性高,以及毛发和尺子标记物的覆盖,对皮肤镜下皮肤图像进行黑色素瘤分类是一项要求很高的工作。本研究在对黑色素瘤皮肤癌进行正确分类的基础上,提出了恶性黑色素瘤皮肤癌分类体系。该系统包括三个主要步骤:分割、特征提取和分类。在分割之前,对预处理后的皮肤病变图像进行处理,去除被覆盖的毛发伪影。在分割步骤中,使用本文提出的基于纹理滤波器的分割方法对输入的预处理病变图像进行分割。然后,利用形状、边缘、颜色和纹理特征从分割的区域中提取具有ABCD(不对称、边界、颜色、差分纹理)皮肤学规则的特征。最后,利用袋树集成分类器对提取的特征进行分类,识别皮肤图像是恶性黑色素瘤还是非黑色素瘤。系统性能评估使用基准数据集:PH2数据集,ISBI2016数据集和ISIC2017数据集。根据实验结果,所提出的设计既可以对真实世界的皮肤镜图像进行可靠的分类,又可以在当今标准PC计算平台上实现可行的操作时间。为了解决数据集中的类不平衡问题,提高分类性能,实验不仅分析了原始不平衡数据集,还分析了平衡数据集:欠采样和过采样数据集。实验结果表明,该系统在使用袋树集成分类器的过采样数据集上运行良好,具有较高的灵敏度和特异性,在统计性能上优于原始不平衡数据集。
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