Medical diagnosis using machine learning techniques has great attention over the last two decades. The detection of skin cancer based on visual information requires highly skilled dermatologists, and also it is a time-consuming process. To analyse the condition of patients and diagnose the diseases at the earliest, an automated classification system is needed that may help to enhance the clinical decision. There are many clinical trials available to classifying melanoma skin cancer. In this study, soft computing techniques are enabled such as image processing, Genetic Algorithm (GA) and Deep Learning Neural Network (DLNN) to get accurate result of classification. To achieve this, three major modules are developed. The first part is dermoscopic image preprocessing from which the dermoscopic images can be prepared by removing noises and skin hairs. The second one is feature extraction and selection. The former one utilizes 3-dimensional Discrete Wavelet Transform (3DWT), and later one uses GA. The last module is the knowledge discover step where the dermoscopic images are classified using an appropriate DLNN classifier. In the result of comparative analysis, a maximum accuracy of 98.67% is obtained using the proposed system on PH² database and 94.50% on International Skin Imaging Collaboration (ISIC) 2017 database.

在过去的二十年中，使用机器学习技术进行医学诊断备受关注。基于视觉信息的皮肤癌的检测需要熟练的皮肤科医生，这也是一个耗时的过程。为了分析患者的状况并尽早诊断疾病，需要一个自动分类系统，这可能有助于增强临床决策。有许多可用于对黑色素瘤皮肤癌进行分类的临床试验。在这项研究中，启用了诸如图像处理，遗传算法（GA）和深度学习神经网络（DLNN）之类的软计算技术，以获取准确的分类结果。为此，开发了三个主要模块。第一部分是皮肤镜图像预处理，从中可以通过去除噪音和皮肤毛发来制备皮肤镜图像。第二个是特征提取和选择。前者使用3D离散小波变换（3DWT），而后者则使用GA。最后一个模块是知识发现步骤，其中使用适当的DLNN分类器对皮肤镜图像进行分类。在比较分析的结果中，使用所提出的系统在PH上获得的最大精度为98.67％^2个数据库，国际皮肤影像协作（ISIC）2017数据库的94.50％。