Leveraging the Novel MSHA Model: A Focus on Adrenocortical Carcinoma

Adrenocortical Carcinoma (ACC) can be detected and diagnosed using CT scans with the help of deep learning models, such as the novel MSHA model. The dataset used in the study consists of 53 patients with verified ACC and their contrast-enhanced CT scans. The MSHA model combines various approaches based on attention-based contextual information with mixed-scale dense convolution, a self-attention mechanism, and hierarchical feature fusion. Evaluation criteria are utilized to assess the model’s performance, demonstrating outstanding precision, sensitivity, specificity, and an F1 score of $\mathbf{9 6. 0 \%}$, along with an accuracy of 96.65%. This performance surpasses well-established models such as ResNet50, VGG16, VGG19, and InceptionV3 and proves valuable for detecting and diagnosing ACC, enhancing patient care and outcomes.