Iterative magnitude pruning-based light-version of AlexNet for skin cancer classification

Abstract

Convolutional Neural Networks (CNN) with different architectures have shown promising results in skin cancer diagnosis. However, CNN has a high computational cost, which makes the need for a light version of CNN a desirable step. This version can be used on small devices, such as mobile phones or tablets. A light version can be created using pruning techniques. In this study, iterative magnitude pruning (IMP) is utilized. This method depends on pruning the network iteratively. The IMP method is applied on AlexNet with transfer learning (TL) and data augmentation. The proposed IMP AlexNet with TL is applied on three different skin cancer datasets which are PAD-UFES-20, MED-NODE, and PH2 dataset. The datasets used are a combination of smartphone, dermoscopic, and non-dermoscopic images. Different CNN versions are applied on the same datasets for comparison with IMP AlexNet. The CNNs used are VGG-16, ShuffleNet, SqueezNet, DarkNet-19, DarkNet-53, and Inception-v3. The proposed IMP AlexNet achieved accuracies of 97.62%, 96.79%, and 96.75%, with accuracy losses of 1.53%, 2.3%, and 2.2%, respectively, compared to the original AlexNet. In addition, the proposed IMP AlexNet requires less running time and memory usage than the traditional AlexNet. The average running time for IMP AlexNet is 0.45 min, 0.28 min, and 0.3 min, for PAD-UFES-20, MED-NODE, and PH2 datasets, respectively. The average RAM usage with IMP AlexNet is 1.8 GB, 1.6 GB, and 1.7 GB, respectively. IMP AlexNet accelerates the average running time by approximately 15 times that of the traditional AlexNet and reduces the average RAM used by 40%.