Dual path sub-type diagnostic model based on the anatomic morphological feature

Abstract

Intracranial hematoma can compress the brain tissue, resulting in increased cranial pressure and a high fatality rate. Therefore, providing a rapid and accurate clinical diagnosis and treatment plan is a crucial step. Intracranial hematoma can be classified into clinical sub-types such as cerebral lobes, ventricular, basal ganglia, pons, and cerebellum hemorrhage based on the location of the hemorrhage. To accurately identify different sub-types and provide targeted treatment, this paper proposes a dual path sub-type diagnosis model based on the anatomic morphological feature. It extracts anatomic morphological features of hematoma based on clinical prior knowledge to achieve prior feature enhancement. Then, a dual-path input model is constructed, and the dual-path module is used to analyze and integrate various features of different sub-types of intracranial hematoma. The model is validated on the clinical data set of the Second Affiliated Hospital of Dalian Medical University, and the corresponding accuracy can reach 0.993. This model can automatically recognize the sub-type of the clinical intracranial hematoma cases and improve diagnostic efficiency. Besides, it further aid in image analysis and enable specific treatment planning.