Classification of Melanocytic Nevus Images using BigTransfer (BiT)
This work addresses the need for automated, efficient diagnosis of skin cancer, specifically for classifying melanocytic nevi to aid in early detection, though it appears incremental as it applies an existing transfer learning method to a specific medical imaging task.
The researchers tackled the problem of classifying melanocytic nevi as benign or malignant using BigTransfer (BiT), a ResNet-based transfer learning approach, and demonstrated that their method outperformed existing techniques in classification rate.
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.