Classification of Melanocytic Nevus Images using BigTransfer (BiT)

TL;DR

This paper presents a transfer learning approach using BigTransfer (BiT) with ResNet for automated classification of melanocytic nevus images, significantly improving accuracy over existing methods for early skin cancer diagnosis.

Contribution

The study introduces a novel application of BiT transfer learning for melanocytic nevus classification, demonstrating superior performance compared to current techniques.

Findings

BiT-based model outperforms existing classification methods

Transfer learning improves diagnostic accuracy for skin lesions

Automated system enables efficient early detection of melanoma

Abstract

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.