Diet-SRNet: A Novel DieT Transformer-Based Model for Accurate Uterine Neoplasm Classification
Authors: Al-Mnayyis Asma’a Mohammad , Amin Mohammad , Mhanna Hamad Yahia Abu , Almahmoud Rawan Eimad , Madain Rola , Alshdaifat Eman Hussein , Akhdar Hanan Fawaz , Gharaibeh Hasan , Alhatamleh Salem , Nasayreh Ahmad , Gharaibeh Mohammad 
Published: January 2025

Abstract

Uterine tumors are atypical cell proliferations within the uterus. These tumors can be classified as benign (non-cancerous) or malignant (cancerous). Rapid and accurate diagnosis and classification of uterine malignancies are essential for improving patient care and formulating successful treatment protocols. Medical imaging methods are critical for identifying uterine malignancies. However, achieving accurate diagnosis remains a hurdle. This study presents three uterine tumor CT image datasets (KAUH-UCCTD, KAHU-BUD, and KAUH-MUTD) collected at King Abdullah University Hospital in Jordan. The images were inspected and clustered by medical professionals. A novel image classification architecture called Diet-SRNet was developed. This model integrates advanced mechanisms based on transformers and convolutional neural networks (CNNs) to address the limitations of current methods. The infrastructure is designed to efficiently process CT scan data, incorporating self-attention mechanisms for global feature interactions and residual convolutional blocks (RCBs) to extract visual data and transform representations efficiently. The residual blocks include multiple convolutional layers, which enhance the model’s ability to capture local spatial relationships and improve the representation of hierarchical features. The self-attention mechanisms within the Diet-SRNet model play a pivotal role in enabling global feature pooling, which is essential for processing high-dimensional image data. These mechanisms compute the relationships between different image regions, allowing the model to focus on critical tumor features. Meanwhile, the residual convolutional blocks efficiently process low-level and mid-level features, enabling robust transformations that lead to more accurate classification of benign, malignant, and normal tumors. The Diet-SRNet architecture is further enhanced by local embedding layers, which enhance spatial representation, and a hybrid attention mechanism, which combines self-attention and residual connections. The developed model, named Diet-SRNet, demonstrated superior performance compared to eight pre-trained models DieT Transformer and ViT Transformer. Diet-SRNet achieved an accuracy of 88.10% on the uterine cancer CT dataset (KAUH-UCCTD), 91.72% on the benign uterine pathology dataset (KAUH-BUD), and 97.11% on the malignant uterine tumor dataset (KAUH-MUTD), outperforming all other trained models, confirming its effectiveness in staging uterine tumors on CT. The proposed strategy reduces the time required for manual diagnosis by helping clinicians quickly and accurately detect benign and malignant uterine tumors using CT data. In oncology, this is critical as it facilitates early intervention. Integrating Diet-SRNet into the clinical workflow can enhance radiologists' work's diagnostic efficiency and consistency.