Deep learning convolutional neural networks can be trained to automatically detect and classify 2D MRI brain tumor images. CNNs use multiple layers including convolutional and pooling layers to extract features from MRI images and classify tumors. Researchers trained CNNs on a large database of MRI images and then classified tumors using machine learning classifiers with CNNs achieving high accuracy in detecting and diagnosing brain tumors from MRI scans.

1. Applications of Deep
learning in MRI Scan

2. Automatic detection and classification of the 2D MRI brain tumor
images using deep convolutional neural networks
• CNNs are trained using large collections of diverse images
• Convolutional neural network is composed of multiple building
blocks, such as convolution layers, pooling layers, and fully
connected layers
• The convolutional layers are used to convolve the input image
with kernels (weights) to obtain a feature map
• Extract the features using CNN and then classifying using four
machine learning classifiers such as SVM, K-NN, naïve bayes and
discriminant analysis classifiers
• Database used : Cancer imaging archive
(https://www.cancerimagingarchive.net/)
3/10/2024 Department of Biomedical Engineering, SRMIST, KTR 2

3. CNN ARCHITECTURE
• Convolution - Convolution of an image with different filters can perform
operations such as edge detection, blur and sharpen by applying filters
• Rectified Linear Unit activation - Relu activation function is used instead of
Tanh to add non-linearity. It accelerates the speed by 6 times at the same
accuracy.
The output is ƒ(x) = max(0,x)
CNN Architecture
3/10/2024 Department of Biomedical Engineering, SRMIST, KTR 3