## 1 Introduction  
Glioblastoma, IDH-wildtype (GBM), classified by the World Health Organization (WHO) as a grade IV glioma [1], represents the most aggressive and prevalent primary malignant brain tumour in adults, accounting for approximately 60% of such cases [2]. The estimated annual incidence of glioblastoma in the United States is 3.26 cases per 100,000 individuals, with age-specific rates increasing progressively: 0.15 in children (0-14 years), 0.58 in adolescents and young adults (15-39), 7.03 in adults over 40, and reaching 15 in the 75-84 age group [3]. Early detection and conventional treatments for GBM have demonstrated limited efficacy, primarily due to its invasive nature and the restrictive properties of the blood-brain barrier [4]. Despite multimodal therapeutic advances, including surgery, radiation, and chemotherapy, the median overall survival remains between 12 and 15 months post-diagnosis, and the 5-year survival rate is only 6.9% [2; 3; 5]. These figures highlight the limitations of standard diagnostics approaches, underscoring the risk of complications and the associated high costs, while emphasising the urgent need for improved diagnostic support systems [6]. Despite being the current diagnostic gold standard, histopathological confirmation of GBM through invasive procedures, such as stereotactic biopsy or surgical resection, presents significant clinical challenges [7]. These methods carry substantial risks, including surgical morbidity (haemorrhage, infection, and neurological deficits), and they typically require prolonged hospitalisation [8]. Furthermore, the intrinsic spatial heterogeneity of GBM tumours often leads to sampling bias during biopsy, compromising diagnostic accuracy and limiting the reproducibility of the technique [9]. This biological constraint, coupled with procedural risks, significantly hinders effective longitudinal monitoring of tumour progression and treatment response. This underlines the need to develop safer and non-invasive diagnostic tools, particularly for patients in advanced stages of the disease [10]. Magnetic resonance imaging (MRI) has emerged as a crucial and non-invasive technique for the diagnosis of GBM, offering precise details on tumour morphology and demonstrating its value in neuro-oncology [11; 12]. In addition to its application in GBM diagnosis, MRI has been widely used to characterise various other cancers, including gastric, lung, and colon cancer, proving its versatility and diagnostic value [13; 14; 15]. Furthermore, MRI contributes to increased diagnostic precision and safety, particularly when incorporated with deep learning techniques [16] to improve the detection of cervical cancer [17]. The integration of deep learning into the medical field has led to significant advances by improving the detection and treatment of brain tumours, including GBM [18]. Deep learning methods have shown encouraging results in a range of computer vision tasks, including segmentation, classification, and object detection [11; 19; 20]. These methods employ convolutional layers capable of extracting a hierarchical spectrum of features from input images, ranging from low-level local features to high-level global representations. In the final stage, a fully connected layer processes the extracted feature maps, transforming them into probabilistic predictions associated with specific labels [19]. However, achieving robust and reliable performance requires the careful design of optimal layer architectures, along with the meticulous fine-tuning of hyperparameters to enhance accuracy and generalisation capabilities [11; 19]. To advance the frontier of medical imaging, the Radiological Society of North America (RSNA) and the Medical Image Computing and Computer-Assisted Intervention (MICCAI) have curated a detailed data set, hosted on Kaggle, featuring 585 patients [21].