DMA-UNet: A Dual-Branch Multi-Scale Attention U-Net for MRI Segmentation and Classification of Parotid Gland Tumours

The human salivary glands are classified as crucial components of the exocrine system, which are responsible for generating the saliva required for lubrication, digestion, and oral health, digestion, and maintaining oral health. The tumours of the parotid glands (PGTs) represent a significant clinical concern in head and neck oncology, and both benign versus malignant parotid gland tumours occur. The traditional methods of diagnosis, including the use of cytology with fine-needle aspiration (FNAC), are limited, and such limitations are the inconsistent sensitivity and the invasive nature of the method. This paper proposes a novel architecture built upon a multi-scale dual-branch U-Net to balance between the segmentation and classification of parotid gland tumours in parotid gland MRI. The model consists of a single encoder branch to obtain multi-scale features, while two distinct decoder branches facilitate simultaneous tumour segmentation and classification into normal, benign, and malignant classes. The attention mechanisms improve the model’s performance regarding emphasis on the most relevant imaging features. Preprocessing of a diverse dataset of 332 patients, which included different tumour types in MRI, was carried out and augmented to address class imbalance. The Dual Branch U-Net with Multi-Scale Attention was evaluated for segmentation and classification tasks using validation and unseen test datasets. The model demonstrates robust performance, with accuracy, precision, recall, and F1-score values steadily above 0.96 for both validation and testing datasets, while segmentation results showed perfect Dice coefficients of 0.9988 but extremely high IoU values of 0.9984, indicating effective region-level segmentation with some boundary limitations. This indicates that the model generalizes well and can classify tumour samples with high reliability. Category-wise, the model performs exceptionally well in identifying malignant tumours, achieving near-perfect precision and recall.