  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.
