1. MALLA REDDY ENGINEERING COLLEGE
AN AUTONOMOUS INSTITUTE
NBA & NAAC A++ ACCREDITED
DHULAPALLY , SECUNDERABAD-500 100
BRAIN TUMOR DETECTION AND DIAGNOSIS
BATCH NUMBER: C6
1.SAIRAM GUGULOTHU (19J41A04D7)
2.PRAVEEN BOGAM (19J41A04C8)
3. ADITYA WALIA (19J41A04C1)
4. RUSHMITHA MANDADI (19J41A04F8)
Under the Guidance of
Mrs. C.SILPA
Associate Professor
Department of Electronics and Communication Engineering
2. OUTLINE
1. Objective
2. Introduction
3. Literature Survey
4. Block diagram
5. Principle and working flow
6. Flow chart
7. Advantages
8. Applications
9. Future scope
10. References
3. OBJECTIVE
outline the current state of the art for automatic
segmentation of the brain tumor using convolution
neural network (CNN).
explain how CNN are trained to segment tumors in
MRI images
identify the challenges in developing an automated
segmentation techniques
discuss the potential applications of an automate
segmentation technique
4. INTRODUCTION
Brain tumors are a major cause of death worldwide. Accurate
diagnosis and segmentation of brain tumors is important for accurate
treatment and prognosis. Traditional manual segmentation is time
consuming and tedious, and can lead to inaccurate segmentation.
This is where automatic segmentation techniques with Convolutional
Neural Networks (CNN) can help. CNNs are powerful deep learning
architectures that can be used to accurately segment brain tumors in
MRI images. This paper presents a review of the current state-of-the-
art techniques for automatic segmentation of brain tumors in MRI
images. It will discuss the segmentation methods, including the
architecture, datasets, evaluation metrics and results. In addition, this
paper will also discuss the potential applications and future directions
of this technology.
5. LITERATURE SURVEY
The automatic segmentation of brain tumors in Magnetic Resonance
Imaging (MRI) images has been the focus of much research in the past few
decades. The use of Convolutional Neural Networks (CNNs) has been seen
as a promising tool for this task. This literature survey provides an
overview of the state-of-the-art CNN-based approaches for the automatic
segmentation of brain tumors in MRI images. The first CNN-based models
for brain tumor segmentation were introduced in 2011. These models
utilized a 2D U-Net architecture, which consists of an encoder, a decoder,
and skip connections between the two parts. The encoder is used to
extract features from the input images, while the decoder is used to
reconstruct the segmentation maps. The skip connections allow for
information to flow between the encoder and the decoder, which helps to
improve the segmentation accuracy.Since then, many other models have
been proposed to further improve the accuracy of brain tumor
segmentation in MRI images. These models include 3D U-Nets, Fully
Convolutional Networks (FCNs), and a variety of other approaches.
6. In recent years, the performance of CNN-based methods for brain tumor
segmentation has been further improved by utilizing advanced architectures
and techniques such as residual networks, attention mechanisms, and deep
supervision.
Furthermore, the use of data augmentation, transfer learning, and ensembles of
multiple models has been explored to further increase the accuracy of the
segmentation results.
Overall, the results of the literature survey demonstrate that CNN-based
approaches have shown great potential for the automatic segmentation of brain
tumors in MRI images.
The approaches employed in the state-of-the-art models have been successful
in achieving high accuracy and robustness, and are continually being improved
by utilizing more advanced techniques.
10. PRINCIPLE AND WORKING FLOW
The principal and working flow of using a convolutional neural network
(CNN) to automatically segment brain tumors in MRI images is as follows:
1. Pre-processing: Pre-processing the MRI images to enhance their quality
and make them suitable for analysis. This includes noise reduction,
histogram equalization, contrast enhancement, etc.
2. Data Augmentation: Generating more data by randomly transforming
existing MRI images to create more training samples. This can include
operations such as flipping, rotating, blurring, etc.
3. Training: Training the CNN model on the augmented dataset using
supervised learning. This includes tuning the parameters of the model to
optimize performance.
4. Testing: Testing the model on a test dataset to evaluate its performance.
5. Segmentation: Using the trained model to automatically segment brain
tumors in MRI images. This involves predicting the tumor region for each
MRI scan.
12. ALGORITHEM
The algorithm for Automatic Segmenting Technique of Brain Tumors
With Convolutional Neural Network (CNN) in MRI Images can be
divided into the following
steps:
1. Data preparation: The first step is to collect the images of brain
MRI scans that contain tumors. The data should be formatted
into a suitable format for CNN input.
2. Pre-processing: The second step is to pre-process the data. This
involves normalizing the data to make sure that it is in the same
range, removing any noise or artifacts, and possibly augmenting the
data by rotating, flipping, or scaling the images.
13. 3. Model building: The third step is to build the CNN model. This involves
selecting an architecture, such as a convolutional neural network, and training
the model to recognize and segment the tumors in the MRI images.
4. Evaluation: The fourth step is to evaluate the performance of the model. This
involves testing the model on a test set of MRI images and calculating the
accuracy, precision, recall, and other metrics.
5. Deployment: The fifth and final step is to deploy the model.
This can involve deploying the model as an API, so that it can be used in real-
time applications. It can also involve deploying the model on a cloud platform
so that it can be used in distributed applications.
15. RESULT
The automatic segmenting technique of brain tumors with
Convolutional Neural Network (CNN) in MRI images has become a
promising approach for medical imaging segmentation. CNNs are
commonly used for medical image segmentation due to their ability
to learn from spatial representations of the data and generate highly
accurate segmentation results. In this technique, a CNN is trained on
a dataset of MRI images to learn the features of the tumor from its
spatial arrangement. Once trained, the CNN can then be used to
automatically segment the tumor from the surrounding tissue. The
resulting segmentation is accurate, and can be used to facilitate
diagnosis and treatment of the tumor. Additionally, this technique
has the potential to reduce the time and effort required to manually
segment tumors.
16. FUTURE SCOPE
The future scope for using Convolutional Neural Network (CNN) for
automatic segmenting of brain tumors in MRI images is very
promising. With the advancements in the field of deep learning, CNNs
have become the most prominent tool for segmentation tasks. CNNs
can be used in combination with other image processing techniques
such as shape based segmentation methods or region growing
techniques to increase the accuracy and reliability of segmentation
results. Furthermore, CNNs can also be used in conjunction with
other machine learning techniques such as reinforcement learning,
which can be used to improve the accuracy of segmentation results.
Additionally, the use of deep learning techniques such as generative
adversarial networks (GANs) can be explored to further improve the
accuracy of segmentation results. Finally, the use of transfer learning
techniques on a larger dataset can be used to further improve the
accuracy and reliability of segmentation results.
17. REFERENCES
1. Meng, X., Chen, C., Sun, Y., Zhang, Y., & Wang, H. (2018).
Automatic segmentation of brain tumors on magnetic resonance
images based on a deep convolutional neural network. BMC medical
imaging, 18(1), 41.
2. Sirinukunwattana, K., Snead, D. R., & Rajpoot, N. M. (2016).
Locality sensitive deep learning for detection and classification of
nuclei in routine colon cancer histology images. IEEE transactions on
medical imaging, 35(5), 1196-1206.
3. Hu, Y., Zhang, Y., Sun, Y., & Meng, X. (2018). Automatic Brain
Tumor Segmentation Using Convolutional Neural Network and
Transfer Learning. 2018 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2018).
