International Conference on Computer and Knowledge Engineering

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Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT

Authors :

Seyed Mohammad Hossein Hashemi¹ Leila Safari² Mohsen Hooshmand³ Amirhossein Dadashzadeh Taromi⁴

1- Institute for Advanced Studies in Basic Sciences (IASBS) 2- University of Zanjan (ZNU) 3- Institute for Advanced Studies in Basic Sciences (IASBS) 4- Institute for Advanced Studies in Basic Sciences (IASBS)

Keywords :

Brain Tumor Diagnosis،Clinical Scenarios،Patient-to-Patient Metric،YOLOv8،Data Efficient Image Transformer،Vision Transformer

Abstract :

Reliable diagnosis of brain tumors remains challenging due to low clinical incidence rates of such cases. However, this low rate is neglected in most of proposed methods. We propose a clinically inspired framework for anomaly-resilient tumor detection and classification. Detection leverages YOLOv8n fine-tuned on a realistically imbalanced dataset (1:9 tumor-to-normal ratio; 30,000 MRI slices from 81 patients). In addition, we propose a novel Patient-to-Patient (PTP) metric that evaluates diagnostic reliability at the patient level. Classification employs knowledge distillation: a Data Efficient Image Transformer (DeiT) student model is distilled from a ResNet152 teacher. The distilled ViT achieves an F1-score of 0.92 within 20 epochs, matching nearteacher performance (F1=0.97) with significantly reduced computational resources. This end-to-end framework demonstrates high robustness in clinically representative anomaly-distributed data, offering a viable tool that adheres to realistic situations in clinics.