Girdhar Nancy, Sinha Aparna, Gupta Shivang
School of Computer Science Engineering and Technology, Bennett University, Greater Noida, UP India.
Amity School of Engineering and Technology, Amity University, Noida, UP India.
Soft comput. 2022 Aug 24:1-20. doi: 10.1007/s00500-022-07406-z.
Research in the field of medicine and relevant studies evince that melanoma is one of the deadliest cancers. It defines precisely that the condition develops due to uncontrolled growth of melanocytic cells. The current trends in any disease detection revolve around the usage of two main categories of models; these are general machine learning models and deep learning models. Further, the experimental analysis of melanoma has an additional requirement of visual records like dermatological scans or normal camera lens images. This further accentuates the need for a more accurate model for melanoma detection. In this work, we aim to achieve the same, primarily by the extensive usage of neural networks. Our objective is to propose a deep learning CNN framework-based model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture, activation functions applied, and the dimension of the input array. Models like Resnet, DenseNet, Inception, and VGG have proved to yield appreciable accuracy in melanoma detection. However, in most cases, the dataset was classified into malignant or benign classes only. The dataset used in our research provides seven lesions; these are and . Thus, through the dataset and various deep learning models, we diversified the precision factors as well as input qualities. The obtained results are highly propitious and establish its credibility.
医学领域的研究及相关研究表明，黑色素瘤是最致命的癌症之一。确切地说，这种病症是由于黑素细胞的不受控制生长而发展起来的。任何疾病检测的当前趋势都围绕着两类主要模型的使用；这些是通用机器学习模型和深度学习模型。此外，黑色素瘤的实验分析还额外需要皮肤病学扫描或普通相机镜头图像等视觉记录。这进一步凸显了对更准确的黑色素瘤检测模型的需求。在这项工作中，我们旨在主要通过广泛使用神经网络来实现这一目标。我们的目标是提出一种基于深度学习卷积神经网络（CNN）框架的模型，通过定制网络架构中的层数、应用的激活函数以及输入数组的维度来提高黑色素瘤检测的准确性。像Resnet、DenseNet、Inception和VGG这样的模型已被证明在黑色素瘤检测中能产生可观的准确率。然而，在大多数情况下，数据集仅被分类为恶性或良性类别。我们研究中使用的数据集提供了七种病变；这些是 和 。因此，通过 数据集和各种深度学习模型，我们使精度因素以及输入质量多样化。所获得的结果非常有利，并确立了其可信度。
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