Deep learning methods have made some achievements in the automatic skin lesion recognition, but there are still some problems such as limited training samples, too complicated network structure, and expensive computational costs. Considering the inherent power-efficiency, biological plausibility and good image recognition performance of spiking neural networks (SNNs), in this paper we make malignant melanoma and benign melanocytic nevi skin lesions classification using convolutional SNNs with unsupervised spike-timing-dependent plasticity (STDP) learning rule. Efficient temporal coding, event driven learning rule and winner-take-all (WTA) mechanism together ensure sparse spike coding and efficient learning of our networks which achieve an average accuracy of 83.8%. We further propose to use feature selection to select more diagnostic features to improve the classification performance of our networks. Our SNNs with feature selection reach an average accuracy of 87.7%. Experimental results show that comparing to CNNs that need to be trained from scratch, our SNNs (with and without feature selection) not only achieve much better classification accuracies but also have much better runtime efficiency. Moreover, although the pretrained CNNs models can achieve similar running time, our proposed SNNs are more stable and easier to use than the pretrained CNNs because we do not need to try many pretrained models any more, and our SNNs also have much better classification accuracies than the pretrained CNNs. In addition, our networks have only three convolutional layers, and the complexity of the model and the parameters that need to be trained in the networks are greatly reduced. Our works show that STDP-based SNNs are very beneficial for the implementation of automated skin lesion classifiers on small portable devices. INDEX TERMS Melanoma recognition, convolutional spiking neural networks, STDP, deep learning.
Spiking neural networks (SNNs) have the advantages of inherent power-efficiency, biological plausibility and good image recognition performance. They are good candidates for medical image classification especially when the labeled training data are limited. In medical image classification, one of the major challenges is the highly class imbalanced problem which causes deep learning networks to bias towards the majority class and poorly recognizes the minority class. Despite that there are some methods for addressing this problem, very few algorithm-level methods exist for SNNs. In this work, we propose an imbalanced reward-modulation spike-timing-dependent plasticity (R-STDP) learning rule for SNNs to solve the medical image class imbalanced problem. We introduce an imbalanced reward coefficient for the R-STDP learning rule to set the reward from the minority class to be higher than that of the majority class, and this reward coefficient can help to set the class-dependent rewards according to the data statistic of the training dataset. Experiment results on three benchmark datasets with imbalanced splits show that our method significantly improves the performance than that of the baseline SNNs and outperforms the compared state-of-the-art methods addressing medical image class imbalanced problem including datalevel and algorithm-level methods. Moreover, our method achieves excellent classification performance on the imbalanced medical dataset ISIC-2018. The results show that the proposed method can well help SNNs in classifying imbalanced medical image datasets. Besides, our proposed method can obtain high sensitivity to disease class by adjusting the reward coefficient, which is very useful for identifying disease samples in medical diagnostic tasks.INDEX TERMS Class imbalance, spiking neural networks (SNNs), reward-modulated spike-timingdependent plasticity (R-STDP), medical image.
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