Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Skin lesion recognition is one of the most important tasks in dermoscopic image analysis. Current Convolutional Neural Network (CNN) algorithms based recognition methods tend to become a standard methodology to fix a large array of Computer-Aided Diagnosis (CAD) and interpretation problems. Besides significant practical and theoretical improvements in their architecture, their effectiveness is built on the existence of the flexible pre-trained models which generalize well to novel tasks and handle the problem of having small set of dermoscopic data. However, existing works pay little attention to exploring the benefits of hierarchical multi-feature fusion for classifying the skin lesions in digital dermoscopic images. Practically, it has been found that integrating multi-layer features has significant potential for improving performance of any pattern recognition task. In this paper, we developed a robust CAD system based on transfer learning and multi-layer feature fusion network to diagnose complex skin diseases. It is a convenient approach in terms of overfitting prevention, convergence speed and high morphological feature similarity processing. Our research focuses exclusively on obtaining optimal performance with addressing the various gaps in the skin pattern recognition area. For validation and comparison purposes, the proposed approach was evaluated on publicly dermoscopic dataset, and achieved the high recognition precision compared with fully trained CNN models, fine-tuning process, single CNN model and other related works. Therefore, the study demonstrates that our proposed approach can dramatically improve the performance of CAD systems which are based on the conventional recognition and classification algorithms for skin lesion recognition in dermoscopic data.