IMT-LUSS: A Novel Inception Meets Transformer-based Lung Ultrasound Scoring Model in Pneumonia

Pneumonia is a contagious disease that poses a great threat to human health. The real-time and free-radiation of lung ultrasound (LUS) makes it an essential tool for diagnosing pneumonia. This paper aims to explore an automatic detection model based on lung ultrasound imaging, called IMT-LUSS, which can achieve effective lung scoring. The model combines inception with transformer, intends to incorporate multi-scale information while considering global information. Firstly, the input is compressed using a stem block. Then it is incorporated into the multi-scale inception meet transformer (IMT) block for information representation, which includes a flexible inception module composed of three convolutional branches with different receptive fields and a pooling branch, and a feature encoding module improved by the multi-head self-attention mechanism with depth-wise convolution. Finally, automatic scoring of LUS images is completed based on feature representation information. 19330 LUS images were employed to verify the proposed IMTLUSS model. Experimental results demonstrate that this model has a great LUS scoring performance with high accuracy of 99.44 ± 0.14%. Meanwhile, the ablation experiments on structure and comparative experiments with other models proved its significant superiority, indicating the potential in future clinical applications.