# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 2 Materials and Methods
Techniques such as bagging, boosting, and stacking combine the output of various models into a collective decision. In the case of brain tumour classification, convolutional neural networks (CNNs) trained on segmented regions of the tumour are utilised to extract discriminatory features that are then fed into ensemble algorithms for final classification [52]. The process of combining segmentation methods with deep learning ensemble algorithms includes the following steps:  
Segmentation of tumour areas: Utilize suitable segmentation techniques to identify tumour regions within MRI images. This involves preprocessing the data, applying appropriate segmentation algorithms, and creating segmented masks for the tumour areas [51]. Feature extraction: Extract features from segmented tumour regions using deep learning models such as CNNs. Train these models on the segmented regions to learn features that can capture patterns and characteristics specific to brain tumours [52]. Ensemble learning: Use ensemble learning techniques to combine the predictions of multiple deep learning models. This could involve training different CNN architectures with different initialization parameters, and then combining their predictions by averaging, voting, or other ensemble methods [55]. Classification: Use the combined predictions from the ensemble to classify tumours into different categories. The final classification can be based on the consensus of the individual models' predictions, with each model's contribution weighted according to its performance or confidence level [53]. By integrating appropriate segmentation techniques with deep learning ensemble algorithms, researchers seek to enhance the accuracy, dependability, and generalizability of MRI-driven brain tumour classification models. This integrated strategy enables more accurate tumour analysis, facilitating improved treatment planning and patient care in neuro-oncology [51; 56]. The study employed transfer learning with five pre-trained CNN models: AlexNet, VGG16, GoogleNet, ResNet18, and ResNet50, all originally trained on the ImageNet dataset. It also provides a detailed discussion of each model's architecture:  
1. AlexNet: [PERSON] introduced AlexNet in the 2012 Large-Scale Visual Recognition Challenge (ILSVRC). It won the competition with a top-five error rate of 15.3%, surpassing the state-of-the-art models of that time, which had error rates of 10.8% or higher [51]. Initially trained on two GPU machines, AlexNet now requires only one GPU. It is a relatively shallow network with eight layers, comprising five convolutional layers (Conv) followed by three fully connected (FC) layers [52]. The architecture includes layers with three different filter sizes (11 \(\times\) 11, 5 \(\times\) 5, and 3 \(\times\) 3), data augmentation, dropout, and max pooling operations [53]. Notably, the traditional sigmoid activation function \(SF(x)\) was replaced with the rectified linear unit (ReLU) activation function to address the vanishing gradient problem associated with sigmoid functions [53]. This change enhanced the training stability of the model by preventing the learning process from halting when the gradient approaches zero using the following equations [52]:  
1. Sigmoid function (Equation (1)): \[SF(x)=\frac{1}{1+exp\ (-x)}\] (1)
2. Rectified linear unit (ReLU) function (Equation (2)): \[Re\ Lu(x)=Max(0,x)\] (2)  
The transition from the sigmoid to the ReLU activation function was due to the vanishing gradient problem associated with the sigmoid function. The gradients become very small as inputs move away from zero, causing the learning process to stall. In contrast, ReLU produces non-zero gradients for positive inputs, which prevents gradient saturation and speeds up learning [55]. 2. VGG16: VGGNet, developed by the Visual Geometry Group (VGG) Lab at Oxford University in 2014, secured top positions in the ILSVRC 2014 challenge [53]. Designed by [PERSON] and [PERSON], VGGNet achieved a top 5% test accuracy score of 92.7%.