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

• URL: http://arxiv.org/abs/2401.03302v4
• Date: Tue, 01 Jul 2025 15:31:37 GMT
• Title: Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
• Authors: Seyed Mohammad Hossein Hashemi, Leila Safari, Mohsen Hooshmand, Amirhossein Dadashzadeh Taromi,
• Abstract summary: We propose a clinically inspired framework for anomaly-resilient tumor detection and classification.<n> Detection leverages YOLOv8n fine-tuned on a realistically imbalanced dataset.<n>We also propose a novel Patient-to-Patient (PTP) metric that evaluates diagnostic reliability at the patient level.
• Score: 0.873811641236639
• License: http://creativecommons.org/licenses/by/4.0/
• 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 near teacher 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.

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