# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 2 Materials and Methods
This study uses the VGG16 version, which includes 13 convolutional layers and 3 fully connected layers [52]. It utilises small (3 \(\times\) 3) convolution filters with a stride size of 1 and the same padding throughout all convolutional layers [51]. Additionally, it employs (2 \(\times\) 2) filters for pooling with a stride size of 2. The default input size for images in VGG16 is 224 \(\times\) 224 [52]. The VGG16 model is a convolutional neural network (CNN) architecture proposed by the Visual Geometry Group at the University of Oxford. Its name derives from the fact that it is composed of 16 convolutional and fully connected layers, resulting in a deep network architecture. Despite this considerable depth, VGG16 follows a simple and uniform design, utilising small 3 \(\times\) 3 convolutional filters with max pooling layers interspersed throughout the network. While this approach may seem relatively straightforward, VGG16 had a competitive performance on various image classification benchmarks. Consequently, the VGG16 architecture has been widely adopted as a baseline model in numerous studies and applications across the field of computer vision [12]. GoogleNet (Inception v1):  
GoogleNet, introduced by Google's research group in 2014 with the paper \"Going Deeper with Convolutions\", secured first place in the ILSVRC 2014 competition with a remarkable top 5 error rate of 6.67%. With 22 layers, GoogleNet's design was inspired by LeNet. Its architecture uses tiny (1 \(\times\) 1) convolution filters to reduce the number of intermediate parameters, lowering the parameter count from 60 million in AlexNet to 4 million [53]. In 2014, researchers at Google developed GoogleNet, also known as Inception v1, a convolutional neural network (CNN) architecture. The critical characteristic of GoogleNet is its deep and wide architecture, which features multiple parallel convolutional pathways. The core innovation of this model was the introduction of inception modules, which perform convolutions with multiple filter sizes and concatenate the output feature maps. By leveraging diverse receptive fields at different scales, GoogleNet achieved competitive performance using fewer parameters than traditional CNN architectures. The inception module concept has been further refined in subsequent versions of the model, such as Inception v2 and Inception v3, to improve efficiency and performance. Some of the other notable architectural choices in GoogleNet use 1 \(\times\) 1 filters to limit the number of parameters, the incorporation of global average pooling at the end to reduce feature map size and increase accuracy while decreasing trainable parameters, and use inception modules that employ fixed convolutions of different sizes (1 \(\times\) 1, 3 \(\times\) 3, 5 \(\times\) 5) along with 3 \(\times\) 3 max pooling. This multi-scale approach helps the model effectively manage objects at various scales. Additionally, GoogleNet introduced intermediate classifier branches, such as the Auxiliary Classifier, to provide regularisation and address vanishing gradient issues during training. These innovations have contributed to the strong performance and efficiency of the GoogleNet family of models across various computer vision tasks [12]. 4. ResNet18 and ResNet50:  
The Residual Network (ResNet), created by [PERSON] and his team at Microsoft Research, was introduced at ILSVRC 2015. It won the classification task with an impressive 3.57% error rate on the ImageNet test set [52]. ResNet is distinguished by its ability to address the vanishing gradient problem, allowing for the effective training of intense neural networks. One of ResNet's key advantages is its ability to manage deep architectures while minimising computational complexity and training time. ResNet is available in various versions, such as ResNet18, ResNet50, and ResNet101 [53]. In this study, ResNet18 and ResNet50 were employed. ResNet18 consists of 18 layers, while ResNet50 comprises 50 layers.