Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods.

翻译：皮肤癌是一种致命疾病，每年给人类生命造成沉重负担。彩色皮肤图像中不同皮肤病变（如黑色素瘤和痣）之间存在显著相似性，使得识别和诊断更具挑战性。黑色素细胞痣可能恶化为致命性黑色素瘤。因此，当前管理方案涉及切除那些外观可疑的痣。然而，这需要稳健的分类范式来区分良性和恶性黑色素细胞痣。早期诊断需要可靠的自动分类系统，以使黑色素细胞痣分类高效、及时且成功。本研究提出了一种自动分类算法。该技术利用先前针对其他问题训练好的神经网络对黑色素细胞痣图像进行分类。所提方法采用基于ResNet的迁移学习方法BigTransfer( BiT)将黑色素细胞痣分类为恶性或良性。将所得结果与现有技术进行比较，证明新方法的分类率优于现有方法。