Convolutional neural-adaptive networks for melanoma recognition

Abstract

Designing appropriate features for melanoma recognition tasks is an active field of research. Current deep Convolutional Neural Network (CNN) based recognition methods for medical images need collection of large volumes of labeled data in order to train a new CNN. However, this approach implies very long calculation times and high computational costs. Inspired by transfer learning, we are interested in studying efficacy of lower convolutional weights adaptation process for addressing the challenge of small training data sizes in the dermoscopic domain. It is a convenient deep adaptation network in terms of overfitting prevention, convergence speed and high performance achievement. We evaluated our methodology on the publicly dermoscopic dataset such as the International Skin Imaging Collaboration (ISIC) database using 5-fold cross-validation. In comparison with the current state-of-the-art methods, the experiments show that our proposed system provides efficient results, achieving an average area under the receiver operating characteristic curve (AUC) of 96.66%.