A Lightweight Dendritic ShuffleNet for Medical Image Classification

Abstract

ShuffleNetV2 is a lightweight deep learning model architecture designed to achieve efficient neural network performance in resource-constrained environments. Through channel shuffle and units of ShuffleNetV2, the model promotes effective information exchange between different channels, thereby enhancing feature representation and computational efficiency. However, due to its lightweight architecture, further improvements are needed in terms of accuracy, stability, and generalization ability in classification tasks. Dendritic neurons are basic neurons in the nervous system with multiple dendrites responsible for receiving input signals from other neurons. Inspired by the information processing capacity of dendritic neurons, researchers have proposed a new dendritic neuron model and applied it to various traditional deep learning models, achieving outstanding performance in different tasks. Motivated by this, this paper proposes Dendritic ShuffleNetV2 (DShuffleNetV2), which effectively combines the efficient feature extraction characteristics of ShuffleNetV2 with dendritic neuron features, thereby improving the classification performance in medical image classification tasks. To evaluate the performance of this model, image classification experiments are conducted on three different types of medical image datasets. The experimental results demonstrate that, by leveraging the nonlinear features of dendrites and synapses, DShuffleNetV2 significantly outperforms other comparison models in terms of accuracy, precision, recall, and F1 score.