## 3 Results  
### Training of the CNN Model for GBM Detection  
In the RSNA-MICCAI data set, all patient MRI scans were analysed. It was determined that 27.7% did not contain useful diagnostic information, specifically completely black. Consequently, 72.3% of the remaining images, containing diagnostic-relevant information were retained for further analysis. An analysis of the dimensions of these images was conducted, revealing variability not only across different patients but also within the same patients (Figure 4). A resolution of 512 \(\times\) 512 pixels was the most frequently observed, justifying its selection as the image standard dimension for preprocessing. Therefore, all images were resized to 512 \(\times\) 512 pixels due to the predominant frequency in the data set. Images smaller than this resolution were padded, while larger images maintained uniformity. A histogram analysis was performed to assess the distribution of MRI images per patient (Figure 5). The results showed that most patients had eight images. To ensure consistency across patient data and to avoid excluding individuals with fewer available MRI slices, the number of images per patient was standardised to eight, thereby maximising patient inclusion and minimising information loss. This selection, based on the modal value, facilitated the reconstruction of a volumetric representation of the brain from the most informative slices, approximated to a three-dimensional structure. For patients with more than eight images, a binary mask was applied to each image to convert the values 0 to 255 into a binary scale, where pixels with a value greater than 1 were set to 1 and those with a value greater than 0 remained unchanged. This process identified images with the most significant informational content by measuring the area of non-zero  
Figure 4: Distribution of image dimensions used during training, with sizes ranging from 32 \(\times\) 256 up to 768 \(\times\) 768 pixels, represented by the frequency of each dimension. pixels. Only the eight images with the highest information content were retained to ensure uniformity across the data set. In contrast, patients with less than eight images, the image containing the maximum diagnostic information was duplicated until eight images were reached, thereby preserving uniformity across the data set. The impact of CNN depth on the performance of the GBM classification model was systematically analysed. A range of CNN configurations were evaluated, varying both the number of layers and the number of neurons per layer. Each value in the configuration sequence represents the number of neurons in the corresponding layer, illustrating the progressive increase in model complexity. The tested configurations ranged from simpler architectures with fewer layers and neurons [8; 16; 32] to more intricate structures, incorporating additional layers and higher neuron counts [8; 16; 32; 64; 128; 256]. In order to select the optimal architecture, an incremental experiment was conducted, by progressively increasing the number of layers manner and evaluating their impact on the AUC-ROC metric. For each configuration, a different model was constructed using the Adam optimiser and a binary cross-entropy loss function for compilation. The models were evaluated through 5-fold cross-validation (\(k=5\)), wherein the data set was divided into five equal parts, with each subset serving once as the validation set while the remaining as the training sets [39]. This approach ensured that each sample was used for both training and validation, improving robustness and generalisation. The training was performed with a learning rate of \(1\times 10^{-3}\), a batch size of 64, and a total of 200 training epochs. The model converged within 120 epochs, indicating stable performance across different depths. A clear correlation between model performance and network architecture complexity was observed during the evaluation of CNN on the validation set. This complexity was mainly characterised by the number of convolutional layers.