# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 4 Results
The Top-Five Error Rate column indicates the models' accuracy in classifying the top five predictions, with lower percentages signifying higher accuracy. ResNet18 and ResNet50 have the lowest error rates at 3.57%, followed by GoogleNet at 6.67%, VGG16 at 7.3%, and AlexNet at 15.3%. The number of trainable parameters varies significantly among the models, reflecting their complexity and computational demands. VGG16 has the highest number of parameters at 138 million, while GoogleNet is the most parameter-efficient with only 4 million. These CNN architectures are extensively used in various computer vision tasks, such as image classification, object detection, and semantic segmentation. They are potent tools for feature extraction and representation learning, facilitating the development of advanced deep-learning models for various applications [57]. By incorporating these diverse deep learning architectures into ensemble frameworks, researchers can harness the complementary strengths of individual models and improve classification performance. Ensemble learning techniques such as bagging, boosting, and stacking further enhance the robustness and reliability of classification systems by aggregating predictions from multiple models. Through the synergistic integration of segmentation methods and ensemble learning, researchers aim to unlock the full potential of MRI-based brain tumour classification, paving the way for more accurate diagnosis and personalised treatment strategies in neuro-oncology [44]. We fine-tuned the model parameters in our machine learning process to optimise performance and enhance results. For our study, we trained various deep learning models--AlexNet, VGG16, ResNet18, GoogLeNet, and ResNet50--initially using the ImageNet dataset. Sub \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attributes** & **AlexNet** & **VGG16** & **GoogleNet** & **ResNet18** & **ResNet50** \\ \hline Number of Layers & 8 & 16 & 22 & 18 & 50 \\ \hline Input Size & 227 \(\times\) 227 \(\times\) 3 & 224 \(\times\) 224 \(\times\) 3 & 224 \(\times\) 224 \(\times\) 3 & 224 \(\times\) 224 \(\times\) 3 \\ \hline Model Description & Five convolutional layers, 3 FC layers & 13 convolutional layers, 3 FC layers & 21 convolutional layers, 1 FC layer & 17 convolutional layers, 1 FC layer \\ \hline \multirow{3}{*}{Unique Features} & Local Response Normalization, Overlapping Max & Object Localization and Image Classification & 1 \(\times\) 1 Convolution, Global Average Pooling, Inception Module & Skip Connections & \\ \hline Top-Five error rate & 15.3\% & 7.3\% & 6.67\% & 3.57\% & 3.57\% \\ \hline Number of Parameters (millions) & 60 & 138 & 4 & 11.4 & 23.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key attributes and characteristics of the pre-trained CNN architectures used. sequently, we carefully conducted additional training on specialised datasets to adapt these models for the specific tasks of medical image analysis, particularly for the segmentation and classification of brain tumours. Figure 5 shows examples of segmentation outcomes contrasted with the ground truth. The provided image compares the segmentation results and the ground truth for a medical image identified as Brats18_TCIA04_343_1_fair. The comparison is organised in three rows and two columns. * Left column (ground truth): These images represent the manually labelled ground truth, indicating the regions of different tissue types within the brain scan. The colours used are as follows:
* Green: edoema;
* Yellow: non-enhancing solid core;
* Red: enhancing core. * Right column (prediction result): These images represent the automated segmentation results produced by a model. The same colour coding is used as in the ground truth images:
* Green: edoema;
* Yellow: non-enhancing solid core;
* Red: enhancing core. Figure 5: The segmentation results were visualized and compared to the ground truth.