  In the field of medical sciences, reliable detection and classification of
brain tumors from images remains a formidable challenge due to the rarity of
tumors within the population of patients. Therefore, the ability to detect
tumors in anomaly scenarios is paramount for ensuring timely interventions and
improved patient outcomes. This study addresses the issue by leveraging deep
learning (DL) techniques to detect and classify brain tumors in challenging
situations. The curated data set from the National Brain Mapping Lab (NBML)
comprises 81 patients, including 30 Tumor cases and 51 Normal cases. The
detection and classification pipelines are separated into two consecutive
tasks. The detection phase involved comprehensive data analysis and
pre-processing to modify the number of image samples and the number of patients
of each class to anomaly distribution (9 Normal per 1 Tumor) to comply with
real world scenarios. Next, in addition to common evaluation metrics for the
testing, we employed a novel performance evaluation method called Patient to
Patient (PTP), focusing on the realistic evaluation of the model. In the
detection phase, we fine-tuned a YOLOv8n detection model to detect the tumor
region. Subsequent testing and evaluation yielded competitive performance both
in Common Evaluation Metrics and PTP metrics. Furthermore, using the Data
Efficient Image Transformer (DeiT) module, we distilled a Vision Transformer
(ViT) model from a fine-tuned ResNet152 as a teacher in the classification
phase. This approach demonstrates promising strides in reliable tumor detection
and classification, offering potential advancements in tumor diagnosis for
real-world medical imaging scenarios.
