BRAIN TUMOUR IDENTIFICATION USING CONVOLUTIONAL NEURAL

NETWORK

ABSTRACT:
The human brain is the major controller of the humanoid system. The abnormal growth and
division of cells in the brain lead to a brain tumor, and the further growth of brain tumors leads to
brain cancer. In the area of human health, Computer Vision plays a significant role, which
reduces the human judgment that gives accurate results. CT scans, X-Ray, and MRI scans are the
common imaging methods among magnetic resonance imaging (MRI) that are the most reliable
and secure. MRI detects every minute objects. Our paper aims to focus on the use of different
techniques for the discovery of brain cancer using brain MRI. In this study, we performed pre-
processing using the bilateral filter (BF) for removal of the noises that are present in an MR
image. This was followed by the binary thresholding and Convolution Neural Network (CNN)
segmentation techniques for reliable detection of the tumor region. Training, testing, and
validation datasets are used. Based on our machine, we will predict whether the subject has a
brain tumor or not. The resultant outcomes will be examined through various performance
examined metrics that include accuracy, sensitivity, and specificity. It is desired that the
proposed work would exhibit a more exceptional performance over its counterparts.
KEYWORDS: Brain tumor, Magnetic resonance imaging, Adaptive Bilateral Filter,
Convolution Neural Network.

1 INTRODUCTION
Medical imaging is the technique and process of creating visual representations of the interior of
a body for clinical analysis and medical intervention, as well as visual representation of the
function of some organs or tissues. Medical imaging seeks to reveal internal structures hidden by
the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a
database of normal anatomy and physiology to make it possible to identify abnormalities. The
medical imaging processing refers to handling images by using the computer. This processing
includes many types of techniques and operations such as image gaining, storage, presentation,
and communication. This process pursues the disorder identification and management. This
process creates a data bank of the regular structure and function of the organs to make it easy to
recognize the anomalies. This process includes both organic and radiological imaging which
used electromagnetic energies (X-rays and gamma), sonography, magnetic, scopes, and thermal
and isotope imaging. There are many other technologies used to record information about the
location and function of the body. Those techniques have many limitations compared to those
modulates which produce images. An image processing technique is the usage of a computer to
manipulate the digital image. This technique has many benefits such as elasticity, adaptability,
data storing, and communication. With the growth of different image resizing techniques, the
images can be kept efficiently. This technique has many sets of rules to perform in the images
synchronously.

1.1 BRAIN ANATOMY:

The brain tumor is one all the foremost common and, therefore, the deadliest brain diseases that
have affected and ruined several lives in the world. Cancer is a disease in the brain in which
cancer cells ascends in brain tissues. Conferring to a new study on cancer, more than one lakh
people are diagnosed with brain tumors every year around the globe. Regardless of stable efforts
to overcome the complications of brain tumors, figures show unpleasing results for tumor
patients. To contest this, scholars are working on computer vision for a better understanding of
the early stages of tumors and how to overcome using advanced treatment options. Magnetic
resonance (MR) imaging and computed tomography (CT) scans of the brain are the two most
general tests to check the existence of a tumor and recognize its position for progressive
treatment decisions. These two scans are still used extensively for their handiness, and the
capability to yield high-definition images of pathological tissues is more. At present, there are
several other conducts offered for tumors, which include surgery, therapies such as radiation
therapy, and chemotherapy. The decision for which treatment relies on the many factors such as
size, kind, and grade of the tumor present in the MR image. It’s conjointly chargeable for
whether or not cancer has reached the other portions of the body. Precise sighting of the kind of
brain abnormality is enormously needed for treatment operations with a resolution to diminish
diagnostic errors. The precision is often makeshift utilizing computer-aided diagnosis (CAD)
systems. The essential plan of computer vision is to produce a reliable output, which is an
associate estimation to assist medical doctors in image understanding and to lessen image
reading time. These advancements increase the steadiness and correctness of medical diagnosis
however, segmenting an MR image of the tumor and its area itself a very problematic job. The
occurrence of tumors in specific positions within the brain image without distinguishing picture
intensities is an additional issue that makes a computerized detection of brain tumor and
segmentation a problematic job.

1.2 MOTIVATION FOR THE WORK:

A brain tumor is defined as abnormal growth of cells within the brain or central spinal canal.
Some tumors can be cancerous thus they need to be detected and cured in time. The exact cause
of brain tumors is not clear and neither is exact set of symptoms defined, thus, people may be
suffering from it without realizing the danger. Primary brain tumors can be either malignant
(contain cancer cells) or benign (do not contain cancer cells).
Brain tumor occurred when the cells were dividing and growing abnormally. It is appearing to be
a solid mass when it diagnosed with diagnostic medical imaging techniques. There are two types
of brain tumor which is primary brain tumor and metastatic brain tumor. Primary brain tumor is
the condition when the tumor is formed in the brain and tended to stay there while the metastatic
brain tumor is the tumor that is formed elsewhere in the body and spread through the brain. The
symptom having of brain tumor depends on the location, size and type of the tumor. It occurs
when the tumor compressing the surrounding cells and gives out pressure. Besides, it is also
occurring when the tumor blocks the fluid that flows throughout the brain. The common
symptoms are having headache, nausea and vomiting, and having problem in balancing and
walking. Brain tumor can be detected by the diagnostic imaging modalities such as CT scan and
MRI. Both of the modalities have advantages in detecting depending on the location type and the
purpose of examination needed. In this paper, we prefer to use the MRI images because it is easy
to examine and gives out accurate calcification and foreign mass location.

1.3 PROBLEM STATEMENT:

Our study deals with automated brain tumor detection and classification. Normally the anatomy
of the brain is analyzed by MRI scans or CT scans. The aim of the paper is tumor identification
in brain MR images. The main reason for detection of brain tumors is to provide aid to clinical
diagnosis. The aim is to provide an algorithm that guarantees the presence of a tumor by
combining several procedures to provide a foolproof method of tumor detection in MR brain
images. The methods utilized are filtering, erosion, dilation, threshold, and outlining of the tumor
such as edge detection. The focus of this project is MR brain images tumor extraction and its
representation in simpler form such that it is understandable by everyone.
The objective of this work is to bring some useful information in simpler form in front of
the users, especially for the medical staff treating the patient. The aim of this work is to define an
algorithm that will result in extracted image of the tumor from the MR brain image. The resultant
image will be able to provide information like size, dimension and position of the tumor, and its
boundary provides us with information related to the tumor that can prove useful for various
cases, which will provide a better base for the staff to decide the curing procedure. Finally, we
detect whether the given MR brain image has tumor or not using Convolution Neural Network.

2. LITERATURE SURVEY
 A. Sivaramakrishnan And Dr. M. Karnan “A Novel Based Approach for Extraction Of
Brain Tumor In MRI Images Using Soft Computing Techniques,” International Journal
Of Advanced Research In Computer And Communication Engineering, Vol. 2, Issue 4,
April 2013.
A. Sivaramakrishnan et al. (2013) [1] projected an efficient and innovative discovery of the brain
tumor vicinity from an image that turned into finished using the Fuzzy Capproach grouping
algorithm and histogram equalization. The disintegration of images is achieved by the usage of
principal factor evaluation is done to reduce the extent of the wavelet coefficient. The outcomes
of the anticipated FCM clustering algorithm accurately withdrawn tumor area from the MR
images.
 Asra Aslam, Ekram Khan, M.M. Sufyan Beg, Improved Edge Detection Algorithm for
Brain Tumor Segmentation, Procedia Computer Science, Volume 58,2015, Pp 430-437,
ISSN 1877-0509.
M. M. Sufyan et al. [2] has presented a detection using enhanced edge technique for brain-tumor
segmentation that mainly relied on Sobel feature detection. Their presented work associates the
binary thresholding operation with the Sobel approach and excavates diverse extents using a
secure contour process. After the completion of that process, cancer cells are extracted from the
obtained picture using intensity values.

 B.Sathya and R.Manavalan, Image Segmentation by Clustering Methods: Performance

Analysis, International Journal of Computer Applications (0975 – 8887) Volume 29– No.11,
September 2011.
Sathya et al. (2011) [3], provided a different clustering algorithm such as K-means, Improvised
K-means, C-means, and improvised C-means algorithms. Their paper presented an experimental
analysis for massive dat=asets consisting of unique photographs. They analyzed the discovered
consequences using numerous parametric tests.

 Devkota, B. & Alsadoon, Abeer & Prasad, P.W.C. & Singh, A.K. & Elchouemi, A.
(2018). Image Segmentation for Early Stage Brain Tumor Detection using Mathematical
Morphological Reconstruction. Procedia Computer Science. 125. 115-123.
10.1016/j.procs.2017.12.017.
B. Devkota et al. [4] have proposed that a computer-aided detection (CAD) approach is used to
spot abnormal tissues via Morphological operations. Amongst all different segmentation
approaches existing, the morphological opening and closing operations are preferred since it
takes less processing time with the utmost efficiency in withdrawing tumor areas with the least
faults.

 K. Sudharani, T. C. Sarma and K. Satya Rasad, "Intelligent Brain Tumor lesion

classification and identification from MRI images using a K-NN technique," 2015
International Conference on Control, Instrumentation, Communication and
Computational Technologies (ICCICCT), Kumaracoil, 2015, pp. 777-780. DOI:
10.1109/ICCICCT.2015.7475384
K. Sudharani et al. [5] presented a K- nearest neighbor algorithm to the MR images to identify
and confine the hysterically full-fledged part within the abnormal tissues. The proposed work is a
sluggish methodology but produces exquisite effects. The accuracy relies upon the sample
training phase.
 Kaur, Jaskirat & Agrawal, Sunil & Renu, Vig. (2012). A Comparative Analysis of
Thresholding and Edge Detection Segmentation Techniques. International Journal of
Computer Applications.vol. 39.pp. 29-34. 10.5120/4898-7432.
Jaskirat Kaur et al. (2012) [6] defined a few clustering procedures for the segmentation process
and executed an assessment on distinctive styles for those techniques. Kaur represented a scheme
to measure selected clustering techniques based on their steadiness in exceptional tenders. They
also defined the diverse performance metric tests, such as sensitivity, specificity, and accuracy.

 Li, Shutao, JT-Y. Kwok, IW-H. Tsang and Yaonan Wang. "Fusing images with different
focuses using support vector machines." IEEE Transactions on neural networks 15, no. 6
(2004): 1555-1561.
J.T. Kwok et al. [7] delivered wavelet-based photograph fusion to easily cognizance at the object
with all focal lengths as several vision-related processing tasks can be carried out more
effortlessly when wholly substances within the images are bright. In their work Kwok et al.
investigated with different datasets, and results show that presented work is extra correct as it
does not get suffering from evenness at different activity stages computations.

Proposed System

The identification of tumor is a very challenging task. The location, shape and the structure of
tumor varies significantly from patient to patient which makes the segmentation a very
challenging task. In the figure shown below, we have shown some images of the same brain slice
from different patients, which clearly reflect the variation of the tumor. We can clearly see that
the location of the tumor is different in all the images/patients shown below. To make it worse,
the shape and the intra-tumoral structure is also different for all the eight patients/images. In fact,
there can be more than one region of the tumor as can be seen from the images below. This
indeed reflects the complexity of automatic segmentation.
6.1 CONCLUSION:
We proposed a computerized method for the segmentation and identification of a brain tumor
using the Convolution Neural Network. The input MR images are read from the local device
using the file path and converted into grayscale images. These images are pre-processed using an
adaptive bilateral filtering technique for the elimination of noises that are present inside the
original image. The binary thresholding is applied to the denoised image, and Convolution
Neural Network segmentation is applied, which helps in figuring out the tumor region in the MR
images. The proposed model had obtained an accuracy of 84% and yields promising results
without any errors and much less computational time.

REFERENCES
[1] A. Sivaramakrishnan And Dr.M.Karnan “A Novel Based Approach For Extraction Of Brain
Tumor In MRI Images Using Soft Computing Techniques,” International Journal Of Advanced
Research In Computer And Communication Engineering, Vol. 2, Issue 4, April 2013.

[2] Asra Aslam, Ekram Khan, M.M. Sufyan Beg, Improved Edge Detection Algorithm for Brain
Tumor Segmentation, Procedia Computer Science, Volume 58,2015, Pp 430-437, ISSN 1877-
0509.
[3] B.Sathya and R.Manavalan, Image Segmentation by Clustering Methods: Performance
Analysis, International Journal of Computer Applications (0975 – 8887) Volume 29– No.11,
September 2011.
[4] Devkota, B. & Alsadoon, Abeer & Prasad, P.W.C. & Singh, A.K. & Elchouemi, A.. (2018).
Image Segmentation for Early Stage Brain Tumor Detection using Mathematical Morphological
Reconstruction. Procedia Computer Science. 125. 115- 123. 10.1016/j.procs.2017.12.017.
Computer Technology and Applications, ISSN: 2229-6093, Vol. 2, Issue 4, PP. 855- 859 August
2011.
[9] Mahmoud, Dalia & Mohamed, Eltaher. (2012). Brain Tumor Detection Using Artificial
Neural Networks. Journal of Science and Technology. 13. 31-39.
[10] Marroquin J.L., Vemuri B.C., Botello S., Calderon F. (2002) An Accurate and Efficient
Bayesian Method for Automatic Segmentation of Brain MRI. In: Heyden A., Sparr G., Nielsen
M., Johansen P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in
Computer Science, vol 2353. Springer, Berlin, Heidelberg.
[11] Minz, Astina, and Chandrakant Mahobiya. “MR Image Classification Using Adaboost for
Brain Tumor Type.” 2017 IEEE 7th International Advance Computing Conference (IACC)
(2017): 701-705.
[12] Monica Subashini.M, Sarat Kumar Sahoo, “Brain MR Image Segmentation for
TumorDetection using Artificial Neural Networks,” International Journal of Engineering and
Technology (IJET), Vol.5, No 2, Apr-May 2013.
[13] S. Li, J.T. Kwok, I.W Tsang, and Y. Wang, ―Fusing Images with Different Focuses using
Support Vector Machines, Proceedings of the IEEE transaction on Neural Networks, China,
November 2007.
[14] H. Yu and J.L. Fan, ―Three-level Image Segmentation Based on Maximum Fuzzy Partition
Entropy of 2-D Histogram and Quantum Genetic Algorithm, Advanced Intelligent Computing
Theories, and Applications. With Aspects of Artificial Intelligence. Lecture Notes in Computer
Science, Berlin, Heidelberg 2008.
[15] P.S. Mukambika, K Uma Rani, “Segmentation and Classification of MRI Brain Tumor,”
International Research Journal of Engineering and Technology (IRJET), Vol.4, Issue 7, 2017, pp.
683 – 688, ISSN: 2395-0056