A novel weight pruning strategy for light weight neural networks with application to the diagnosis of skin disease

Skin Cancer Classification With Deep Learning: A Systematic Review

Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Convolutional neural network (CNN) models have made tremendous progress in the medical domain in recent years. The application of the CNN model is restricted due to a huge number of redundant and unnecessary parameters. In this paper, the weight and unit pruning strategy are used to reduce the complexity of the CNN model so that it can be used on small devices for the diagnosis of lumbar spondylolisthesis. Experimental results reveal that by removing 90% of network load, the unit pruning strategy outperforms weight pruning while achieving 94.12% accuracy. Thus, only 30% (around 850532 out of 3955102) and 10% (around 251512 out of 3955102) of the parameters from each layer contribute to the outcome during weight and neuron pruning, respectively. The proposed pruned model had achieved higher accuracy as compared to the prior model suggested for lumbar spondylolisthesis diagnosis.