Khan Muhammad Attique, Akram Tallha, Sharif Muhammad, Saba Tanzila, Javed Kashif, Lali Ikram Ullah, Tanik Urcun John, Rehman Amjad
Department of Computer Science and Engineering, HITEC University, Museum Road, Taxila, Pakistan.
Department of Electrical Engineering, COMSATS University Islamabad, Wah Campus, Pakistan.
Microsc Res Tech. 2019 Jun;82(6):741-763. doi: 10.1002/jemt.23220. Epub 2019 Feb 15.
Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK-1, ISIC MSK-2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.
皮肤癌是最致命的癌症类型之一，在过去十年中已在全球范围内广泛蔓延。为了准确检测和分类黑色素瘤，应考虑多种措施，包括对比度拉伸、不规则性测量、选择最优特征等等。病变对比度不佳会影响分割精度并增加分类误差。为克服这一问题，提出了一种用于精确边界检测和分类的有效模型。所提出的模型在预处理阶段提高了分割精度，利用病变区域与背景相比的对比度增强。选择增强后的二维蓝色通道来构建显著性图，最后通过阈值函数生成二值图像。此外，基于粒子群优化（PSO）的分割也用于精确的边界检测和细化。还提取了包括形状、纹理、局部和全局等少数选定特征，随后基于遗传算法进行选择，其优势在于识别最适合的染色体。最后，将优化后的特征输入支持向量机（SVM）进行分类。已在名为PH2、ISBI2016和ISIC（即ISIC MSK - 1、ISIC MSK - 2和ISIC UDA）的三个数据集上进行了综合实验。每个数据集分别获得了97.9%、99.1%、98.4%和93.8%的提高准确率。在所选数据集上，支持向量机在灵敏度、精确率、准确率和误识率方面表现出色。此外，该选择方法表现优异并成功去除了冗余特征。
