As per recent developments in medical science, the skin cancer is considered as one of the common type disease in human body. Although the presence of melanoma is viewed as a form of cancer, it is challenging to predict it. If melanoma or other skin diseases are identified in the early stages, prognosis can then be successfully achieved to cure them. For this, medical imaging science plays an essential role in detecting such types of skin lesions quickly and accurately. The application of our approaches is to improve skin cancer detection accuracy in medical imaging and further, can be automated using electronic devices such as mobile phones etc. In the proposed paper, an improved strategy to detect three type of skin cancers in early stages are suggested. The considered input is a skin lesion image which by using the proposed method, the system would classify it into cancerous or non-cancerous type of skin. The image segmentation is implemented using fuzzy C-means clustering to separate homogeneous image regions. The preprocessing is done using different filters to enhance the image attributes while the other features are assessed by implementing rgb color-space, Local Binary Pattern (LBP) and GLCM methods altogether. Further, for classification, artificial neural network (ANN) is trained using differential evolution (DE) algorithm. Various features are accurately estimated to achieve better results using skin cancer image datasets namely HAM10000 and PH2. The novelty of the work suggests that DE-ANN is best compared among other traditional classifiers in terms of detection accuracy as discussed in result section of this paper. The simulated result shows that the proposed technique effectually detects skin cancer and produces an accuracy of 97.4%. The results are highly accurate compare to other traditional approaches in the same domain.

中文翻译：

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基于模糊C均值聚类的DE-ANN启发式皮肤癌检测方法

根据医学的最新发展，皮肤癌被认为是人体中的常见类型疾病之一。尽管黑素瘤的存在被视为癌症的一种形式，但对其进行预测具有挑战性。如果在早期发现了黑色素瘤或其他皮肤疾病，则可以成功实现预后以治愈它们。为此，医学成像科学在快速，准确地检测此类皮肤损伤中起着至关重要的作用。我们的方法的应用是为了提高医学成像中皮肤癌的检测准确性，并且可以使用移动电话等电子设备实现自动化。在本文中，提出了一种改进的策略，可以在早期阶段检测三种类型的皮肤癌。所考虑的输入是皮肤病变图像，通过使用所提出的方法，系统会将其分类为癌性或非癌性皮肤。图像分割使用模糊C均值聚类来实现，以分离均匀的图像区域。预处理使用不同的滤镜完成，以增强图像属性，而其他功能则通过完全实现rgb颜色空间，局部二进制模式（LBP）和GLCM方法进行评估。此外，对于分类，使用差分进化（DE）算法训练人工神经网络（ANN）。使用皮肤癌图像数据集HAM10000和PH2，可以准确估计各种功能以获得更好的结果。这项工作的新颖性表明，就检测精度而言，DE-ANN在其他传统分类器中是最好的，如本文结果部分所述。仿真结果表明，该技术有效地检测了皮肤癌，准确率达到97.4％。与同一领域的其他传统方法相比，该结果非常准确。