In the last decade, the public health problem is the primary consciousness worldwide, and cancer is especially the central issue. Further, skin cancer comes in the top-3 of the world's most common cancer. We have proposed an efficient convolutional neural network (CNN) model that identifies skin cancer problems accurately. Although dataset HAM10K is used for the classification problem, its samples for each class are highly imbalanced and therefore are accountable for lower training accuracy. The AlexNet model is customized for the HAM10K data classification to address this problem. In addition, this work has presented an activation function that overcomes the vanishing gradient problem, and it is verified using the used dataset at multiple benchmark architectures. The results show better accuracy compared to the state-of-the-art activation function. Our customized CNN architecture with the proposed activation function has 98.20% accuracy for HAM10K, which is better than any other state-of-the-art models currently present. Further, precision, recall, and F-score results are also improved, which are also 98.20%.

中文翻译：

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基于成像技术的准确无创皮肤癌筛查

近十年来，公共卫生问题是世界范围内的首要意识，癌症尤其是中心问题。此外，皮肤癌在世界上最常见的癌症中排名前三。我们提出了一种有效的卷积神经网络 (CNN) 模型，可以准确地识别皮肤癌问题。尽管数据集 HAM10K 用于分类问题，但其每个类别的样本高度不平衡，因此导致训练精度较低。AlexNet 模型是为 HAM10K 数据分类定制的，以解决这个问题。此外，这项工作提出了一种克服梯度消失问题的激活函数，并在多个基准架构中使用使用的数据集进行了验证。与最先进的激活函数相比，结果显示出更好的准确性。我们定制的 CNN 架构与提议的激活函数对 HAM10K 的准确率为 98.20%，优于目前存在的任何其他最先进的模型。此外，精度、召回率和 F-score 结果也得到了提高，也是 98.20%。