This document discusses using convolutional neural networks (CNNs) for brain tumor detection. CNNs have convolutional, pooling, and dense layers that can extract features from medical images like MRIs. The document focuses on a CNN model to classify brain images as either containing a tumor or being healthy. The model is trained on a dataset of 259 brain tumor images and 20 healthy images from Kaggle, with data augmentation used to address class imbalance. The CNN contains convolution, pooling, dropout and dense layers with rectified linear unit activation. It aims to accurately detect tumors in brain MRI images.

1. U S I N G C N N
B R A I N T U M O R D E T E C T I O N
Harik a Satti

2. Introduction
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3. • The brain tumor is taken into account as the very
best reason for death in cancer cases besides
breast cancer. Brain tumor cases have burgeoned
for the last decade in several countries. Medical
imaging is highly important for diagnosing and
preventing more virulent diseases. MRI is widely
used as it doesn’t use ionizing radiation.
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4. Literature Overview
This study focused on Convolutional Neural Network.
Convolutional
Layer
Pooling
Layer
Dense
Layer
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5. Convolutional Neural Network
A convolutional neural network is a neural network that
aims to method information that incorporates a grid
structure. Convolution is an operation within the
convolution layer that’s supported by an algebra
operation that multiplies the matrix of the filter in the
image to be processed [4]. The convolution layer is that
the primary layer that’s most vital to use. Another style
of the layer that is ordinarily used is the pooling layer,
which could be a layer that is wont to take the utmost
worth or the common value of the picture element parts
of the image.
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6. 0
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15
20
25
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0 50 100 150 200 250 300 350
Sample 1 Sample 2 Sample 3 Sample 4
CNN Architecture
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7. The convolution Layer is the core layer within
the CNN methodology that aims to extract
options from the input. Convolution performs
linear transformations of input files while not
dynamical spatial data in the data.
Convolution kernels are determined from the
load of the layer in order that the convolution
kernels will method the input data coaching
on CNN.
CONVOLUTIONAL LAYER
Scientific findings 7

8. Pooling Layer
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Max pooling may be a pooling operation that
selects the utmost component from the region of
the feature map lined by the filter. Thus, the output
once the max-pooling layer would be a feature
map containing the foremost outstanding options
of the previous feature map

9. The dense layer is a neural network layer
that’s connected deeply, which suggests
every nerve cell within the dense layer
receives input from all neurons of its previous
layer. The dense layer helps us to extract the
data or features out of the image and so feed
that into dense layers.
DENSE LAYER
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10. ABOUT THE DATASET
A a
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The dataset employed in this study is Brain magnetic
resonance imaging pictures for tumor Detection obtained
from www.kaggle.com. The dataset consists of 279
images sorted into 2 groups, 259 brain images that have
tumors, and twenty brain images that are healthy.

11. DATA AUGMENTATION
The amount of data within the dataset isn’t enough to be used as
training data for CNN. so the augmentation technique is
employed to beat the imbalance of issues. Augmentation is a
formula which will utilize applied mathematics data information
and kind of an integrated model. This algorithm can manufacture
a variety of two-dimensional pictures of assorted poses and
sizes.
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12. Image Preprocessing
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13. MODEL CNN
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In this research, the CNN model contains many layers,
specifically the convolution layer, the pooling layer, the
flatten layer, the dropout layer, and the dense layer. in
addition to the layers employed in the CNN process,
there’s also an activation function during this study
using rule activation.

14. References
[1]Detecting Brain Tumor using Neural Networks by Lakshmi R Suresh
[2]M. H. Avizenna, I. Soesanti, and I. Ardiyanto, “Classification of Brain Magnetic Resonance Images
Based on Statistical Texture,” Proc. - 2018 1st Int. Conf. Bioinformatics, Biotechnol.Bio med. Eng. Bio
MIC 2018, vol. 1, pp. 1–5, 2019
[3]Convolutional neural networks for brain tumor segmentation by Marc Agzarain
[4] K. P. Danukusumo, Pranowo, and M. Maslim, “Indonesia ancient temple classification using
convolutional neural network,” ICCREC 2017 - 2017 Int. Conf. Control. Electron. Renew.Energy,
Commun. Proc., vol. 2017-January, pp. 50–54, 2017.
[5]Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant
Diseases by Valeria Maeda-Gutiérrez
[6] T. Zhou, S. Ruan, and S. Canu, “A review: Deep learning for medical image segmentation using multi-
modality fusion,” Array, vol. 4, no. July, p. 100004, 2019
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