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:皮肤癌是每年夺去人类生命的致命疾病。有色皮肤图像显示出不同皮肤病变,如黑色素瘤和黑素细胞痣之间具有显著的相似性,使得识别和诊断更具挑战性。黑素瘤不良的黑素细胞痣可能会发展为致命的黑色素瘤。因此,目前的管理协议涉及去除看似具有威胁的黑素细胞痣。然而,这需要强大的分类范式来识别良性和恶性黑素细胞痣。早期诊断需要可靠的自动化系统来对黑素细胞痣进行分类,以使诊断高效、及时和成功。本研究提出了一种自动分类算法。该技术利用了之前在不同问题上训练的神经网络,用于对黑素细胞痣图像进行分类。所提出的方法使用基于ResNet的迁移学习方法——大规模迁移模型(BigTransfer)来将黑素细胞痣分类为恶性和良性。所得结果与当前方法进行了比较,并证明了新方法的分类率优于现有方法。