A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi
This work addresses skin cancer classification for medical applications, but it appears incremental as it compares existing methods without introducing new approaches.
The study performed a comparative analysis of five transfer learning-based deep convolutional neural network techniques for classifying melanocytic nevi to aid in early skin cancer diagnosis, but no concrete results or numbers were reported.
Skin cancer is a fatal manifestation of cancer. Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. To deal with lethal mortality rates coupled with skyrocketing costs of medical treatment, early diagnosis is mandatory. To tackle these challenges, researchers have developed a variety of rapid detection tools for skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. In this study, a comparative analysis has been performed on five Transfer Learning-based techniques that have the potential to be leveraged for the classification of melanocytic nevi. These techniques are based on deep convolutional neural networks (DCNNs) that have been pre-trained on thousands of open-source images and are used for day-to-day classification tasks in many instances.