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e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[394]
R-PI BASED DETECTION OF LUNG CANCER USING MRI IMAGE
Miss Parina Gulab Shikalgar*1, Dr. B. G. Patil*2
*1PG Student, Electronics Department, Walchand College Of Engineering Sangli, Maharashta, India.
*2Professor, Electronics Department, Walchand College Of Engineering, Sangli, Maharastra, India.
ABSTRACT
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being.
Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the
most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells
are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing
medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a
time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior
sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung
cancer and this method would be excellent of lung malignancies and other diseases.
Keywords: Lung Cancer, Magnetic Resonance Imaging, Raspberry Pi.
I. INTRODUCTION
Due to lung disease the death rate among people is increased than any other diseases. The survival rate of
patients after detecting lung cancer is minimal as it takes time to detect. If the disease could be detected in the
early stages, then survival rate will be increases. Lung cancer is divided into two groups, non-small cell lung
cancer and small cell lung cancer. The classification is based on their cellular characteristics. Based on tumor
size there are 4 stages of lung cancer. Depending on tumor size and location of lymph node. Earlier detection,
the more will be survival rate of the patient. Due to smoking of cigarette, there are about 85% male and 75%
females are suffering from cancer of lung. The general rate of survival of people suffering from lung cancer is
63%. Surgery, radiation treatment, and chemotherapy are parts of the treatment of lung cancer. Among all
patients suffering from this cancer only 14% patients having the five-year survival rate for all stages. This
situation is not changed in the previous three decades.
Magnetic resonance imaging (MRI) is a one type of scanning that uses strong magnetic field and radio waves to
produce detailed image of the inside of the body. MRIs employs powerful magnets which produces a strong
magnetic field that forces protons in the body to align with that field. When a radiofrequency current is pulsed
through the patient, the protons were stimulated. MRI is non-invasive imaging technology. It produces three-
dimensional detailed image. It is used for diagnosis, disease detection and treatment monitoring.
II. METHODOLOGY
a. Proposed System
The proposed system is used for detection of the lung cancer. For determining CNN algorithm is used. CNN
stands for Convolutional Neural Network. In the first stage, by using MRI image lung regions will be extracted.
Extracted part of image will be given as an input to raspberry-pi. In every stage of system, the tumor will be
segmented. Then the resulted image will be used to test with the patient’s MRI result image. The proposed
system is given below. As shown below, the system will be able to detect lung cancer using MRI scanning.
Figure 1: Proposed system
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[395]
III. MODELING AND ANALYSIS
In this part, the methods which are used for the proposed model is described in detail. The steps of detection of
lung cancer is given in following fig.
Figure 2: Block Diagram of proposed system
Pre-Processing
In pre-processing, the input image is processed to improve the image quality. In this part various operations are
performed on image in which image is enhanced by performing some operations. This enhanced image is used
for further operations. So, it is of doing some operations on image.
Data Pre-processing is a method which is used to convert the raw data into a clear data set. In another words,
when we collect data from many other different sources, it is present in raw data format which we cannot
procced for further analysis.
Image Pre-processing is divided in 3 basic steps such as ‘Image Smoothing’, ’Image Enhancement’ and ‘Image
Segmentation’.
A. Image Smoothing:
The suppressions of high frequencies in the frequency domain is same to suppression of noise or instabilities in
the image. All sharp edges are get blurred during smoothing. Through image blurring, they share the idea for
reduction of noise. Blurring can be done by using Gaussian smoothing model which calculates dissimilarities of
the image. White noise is one of the most common problem in image processing. Even a high-resolution picture
is having a noise. A simple box blur will be sufficient for high resolution picture. Pixel weights is calculated by
Neighborhood filter which depends on color similarities.
B. Image Enhancement:
To improve image information enhancement technique is used. For other image processing techniques, it also
provides better input. Image enhancement is classified in two categories such as spatial domain and frequency
domain.
In the image enhancement, following three techniques are used. They are ’Gabor filter’, ‘Auto-enhancement’ and
‘Fast Fourier Transform’ techniques.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[396]
Figure: Original Image Figure 3: Enhanced Image
C. Image Segmentation:
For analysis of image, image segmentation is used. The image is divided into multiple segments. For
visualization and volume estimation of area of interest, detection of abnormalities (e.g. tumors, polyps, etc.) and
classification, the segmentation of image is very useful for such applications in medical profession. The main
goal of segmentation is to represent the image into more meaningful manner and also it is easy to analyze.
Image segmentation is used to locate objects and boundaries of image. More correctly, the process of assigning
a label to every pixel in an image, image segmentation is used. This results in a set of segments which covers the
entire image. All pixels in a given region are similar with respect to color, intensity, or texture of image.
Adjacent regions are different having same characteristics.
Segmentation algorithms is based on two basic properties of intensity values such as discontinuity and
similarity.
Figure: Enhanced image Figure 4: Segmented image
Extraction of Feature:
This stage is important stage. Here, it uses algorithm and techniques to detect desired part or shape of a given
image. The input data will be transformed into a reduced set of features when the input data set is too large to
be processed. The basic characteristics are area, perimeter and eccentricity of image. These parameters are
measured in scalar. These features are explained as follows,
Area of image:
It is measured in scalar value. It gives actual number of pixel value. It is obtained by the summation of areas of
pixel in the image that is registered as 1. It contains pixels with 255 values.
Perimeter of image:
It is a scalar value. It gives actual number of pixels. In the binary image, it is obtained by summation of inter
connected of registered pixel. Transformation function create array of edge pixels. It is having pixel value of 255
and having at least one pixel with 0 value.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[397]
Eccentricity:
It is also called as roundness or circularity or irregularity. It is equal to 1 only for circular operations and it is
less than 1 for any other shape. The value is closer to 1, when the object is more circular.
Eccentricity= Length of Major Axis/ Length of Minor Axis
Figure: Filtered Image Figure5: Extracted Image
Classification:
For classification, Support vector machines are used. To analyze the data and recognize patterns, Support
vector machines are used. The basic SVM takes a set of input data. SVM uses a kernel function. It is then mapped
the given data into a different space. Then separations of data can be made. It includes different kernel function
such as polynomial, quadratic, Multi-Layer Perceptron (MLP). The Fig of maximum margin hyper planes is
shown below.
Figure 6: Maximum margin classifie
IV. RESULTS AND DISCUSSION
The convolution based neural network is implemented in Python programming. The sample data sets are
trained to understand the model and to detect the tumor of lung cancer. Sample image is fed as an input to our
system and thee model is able to detect lung cancer after processing all the steps in our model. In this process
input image is given to r-pi and displays the output on screen. In case, if cancer is present after performing all
operations then a message will be indicating that cancer is detected on screen.
Software Results:
Case 1: Cancer is present in lungs and it is detected.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[398]
Figure 7: Cancer Region is detected
Figure 8: Screenshot of Terminal window where cancer region is detected
Figure 9: Screenshot of terminal window where cancer is not detected.
V. CONCLUSION
The proposed system is implemented to detect the lung cancer tissue using CNN Algorithm. MRI images having
different shape, size has been fed to the input of proposed system. The system is able to detect the cancer is
present or not.
ACKNOWLEDGEMENTS
I am really grateful to my guide for presenting me with such an innovative dissertation topic. He has always
guided me with his valuable suggestions and encouraged me a lot. I have learned to think deep and critically to
select problems, to solve the problems and to present their solutions. I am thankful to him for always making
time for me through his busy schedule. I feel proud to present my dissertation work under his guidance.
I am also thankful to my panel for their encouragement and support. I thank to all the teaching and non-
teaching staff of my college for providing various facilities. Last but not least I am very thankful to my friends
for always helping me and my parents who have always been the driving force for all my achievements.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[399]
VI. REFERENCES
[1] Ilya Levner, Hong Zhangm ,“Classification driven Watershed segmentation ”, IEEE TRANSACTIONS ON
IMAGE PROCESSING VOL. 16, NO. 5, MAY 2007
[2] Austin J. Sim, Evangelia Kaza, Lisa Singer, Stephen A. Rosenberg- A review of the role of MRI in
diagnosis and treatment of early stage lung cancer.
[3] Ayushi Shukla, Chinmay Parab, Pratik Patil, Prof.Savita Sangam-Lung cancer detection using Image
processing.
[4] Cancer.net
[5] https://www.researchgate.net//publication
[6] Sluimer, A. Schilham, M. Prokop, and B. Van Ginneken, “Computer analysis of computed tomography
scans of the lung: A survey,” IEEE Transactions on Medical Imaging, vol. 25, no. 4, pp. 385–405, 2006.
[7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural
Networks,” Advances In Neural Information Processing Systems, pp. 1–9, 2012.

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R-PI BASED DETECTION OF LUNG CANCER USING MRI IMAGE

  • 1. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [394] R-PI BASED DETECTION OF LUNG CANCER USING MRI IMAGE Miss Parina Gulab Shikalgar*1, Dr. B. G. Patil*2 *1PG Student, Electronics Department, Walchand College Of Engineering Sangli, Maharashta, India. *2Professor, Electronics Department, Walchand College Of Engineering, Sangli, Maharastra, India. ABSTRACT Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases. Keywords: Lung Cancer, Magnetic Resonance Imaging, Raspberry Pi. I. INTRODUCTION Due to lung disease the death rate among people is increased than any other diseases. The survival rate of patients after detecting lung cancer is minimal as it takes time to detect. If the disease could be detected in the early stages, then survival rate will be increases. Lung cancer is divided into two groups, non-small cell lung cancer and small cell lung cancer. The classification is based on their cellular characteristics. Based on tumor size there are 4 stages of lung cancer. Depending on tumor size and location of lymph node. Earlier detection, the more will be survival rate of the patient. Due to smoking of cigarette, there are about 85% male and 75% females are suffering from cancer of lung. The general rate of survival of people suffering from lung cancer is 63%. Surgery, radiation treatment, and chemotherapy are parts of the treatment of lung cancer. Among all patients suffering from this cancer only 14% patients having the five-year survival rate for all stages. This situation is not changed in the previous three decades. Magnetic resonance imaging (MRI) is a one type of scanning that uses strong magnetic field and radio waves to produce detailed image of the inside of the body. MRIs employs powerful magnets which produces a strong magnetic field that forces protons in the body to align with that field. When a radiofrequency current is pulsed through the patient, the protons were stimulated. MRI is non-invasive imaging technology. It produces three- dimensional detailed image. It is used for diagnosis, disease detection and treatment monitoring. II. METHODOLOGY a. Proposed System The proposed system is used for detection of the lung cancer. For determining CNN algorithm is used. CNN stands for Convolutional Neural Network. In the first stage, by using MRI image lung regions will be extracted. Extracted part of image will be given as an input to raspberry-pi. In every stage of system, the tumor will be segmented. Then the resulted image will be used to test with the patient’s MRI result image. The proposed system is given below. As shown below, the system will be able to detect lung cancer using MRI scanning. Figure 1: Proposed system
  • 2. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [395] III. MODELING AND ANALYSIS In this part, the methods which are used for the proposed model is described in detail. The steps of detection of lung cancer is given in following fig. Figure 2: Block Diagram of proposed system Pre-Processing In pre-processing, the input image is processed to improve the image quality. In this part various operations are performed on image in which image is enhanced by performing some operations. This enhanced image is used for further operations. So, it is of doing some operations on image. Data Pre-processing is a method which is used to convert the raw data into a clear data set. In another words, when we collect data from many other different sources, it is present in raw data format which we cannot procced for further analysis. Image Pre-processing is divided in 3 basic steps such as ‘Image Smoothing’, ’Image Enhancement’ and ‘Image Segmentation’. A. Image Smoothing: The suppressions of high frequencies in the frequency domain is same to suppression of noise or instabilities in the image. All sharp edges are get blurred during smoothing. Through image blurring, they share the idea for reduction of noise. Blurring can be done by using Gaussian smoothing model which calculates dissimilarities of the image. White noise is one of the most common problem in image processing. Even a high-resolution picture is having a noise. A simple box blur will be sufficient for high resolution picture. Pixel weights is calculated by Neighborhood filter which depends on color similarities. B. Image Enhancement: To improve image information enhancement technique is used. For other image processing techniques, it also provides better input. Image enhancement is classified in two categories such as spatial domain and frequency domain. In the image enhancement, following three techniques are used. They are ’Gabor filter’, ‘Auto-enhancement’ and ‘Fast Fourier Transform’ techniques.
  • 3. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [396] Figure: Original Image Figure 3: Enhanced Image C. Image Segmentation: For analysis of image, image segmentation is used. The image is divided into multiple segments. For visualization and volume estimation of area of interest, detection of abnormalities (e.g. tumors, polyps, etc.) and classification, the segmentation of image is very useful for such applications in medical profession. The main goal of segmentation is to represent the image into more meaningful manner and also it is easy to analyze. Image segmentation is used to locate objects and boundaries of image. More correctly, the process of assigning a label to every pixel in an image, image segmentation is used. This results in a set of segments which covers the entire image. All pixels in a given region are similar with respect to color, intensity, or texture of image. Adjacent regions are different having same characteristics. Segmentation algorithms is based on two basic properties of intensity values such as discontinuity and similarity. Figure: Enhanced image Figure 4: Segmented image Extraction of Feature: This stage is important stage. Here, it uses algorithm and techniques to detect desired part or shape of a given image. The input data will be transformed into a reduced set of features when the input data set is too large to be processed. The basic characteristics are area, perimeter and eccentricity of image. These parameters are measured in scalar. These features are explained as follows, Area of image: It is measured in scalar value. It gives actual number of pixel value. It is obtained by the summation of areas of pixel in the image that is registered as 1. It contains pixels with 255 values. Perimeter of image: It is a scalar value. It gives actual number of pixels. In the binary image, it is obtained by summation of inter connected of registered pixel. Transformation function create array of edge pixels. It is having pixel value of 255 and having at least one pixel with 0 value.
  • 4. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [397] Eccentricity: It is also called as roundness or circularity or irregularity. It is equal to 1 only for circular operations and it is less than 1 for any other shape. The value is closer to 1, when the object is more circular. Eccentricity= Length of Major Axis/ Length of Minor Axis Figure: Filtered Image Figure5: Extracted Image Classification: For classification, Support vector machines are used. To analyze the data and recognize patterns, Support vector machines are used. The basic SVM takes a set of input data. SVM uses a kernel function. It is then mapped the given data into a different space. Then separations of data can be made. It includes different kernel function such as polynomial, quadratic, Multi-Layer Perceptron (MLP). The Fig of maximum margin hyper planes is shown below. Figure 6: Maximum margin classifie IV. RESULTS AND DISCUSSION The convolution based neural network is implemented in Python programming. The sample data sets are trained to understand the model and to detect the tumor of lung cancer. Sample image is fed as an input to our system and thee model is able to detect lung cancer after processing all the steps in our model. In this process input image is given to r-pi and displays the output on screen. In case, if cancer is present after performing all operations then a message will be indicating that cancer is detected on screen. Software Results: Case 1: Cancer is present in lungs and it is detected.
  • 5. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [398] Figure 7: Cancer Region is detected Figure 8: Screenshot of Terminal window where cancer region is detected Figure 9: Screenshot of terminal window where cancer is not detected. V. CONCLUSION The proposed system is implemented to detect the lung cancer tissue using CNN Algorithm. MRI images having different shape, size has been fed to the input of proposed system. The system is able to detect the cancer is present or not. ACKNOWLEDGEMENTS I am really grateful to my guide for presenting me with such an innovative dissertation topic. He has always guided me with his valuable suggestions and encouraged me a lot. I have learned to think deep and critically to select problems, to solve the problems and to present their solutions. I am thankful to him for always making time for me through his busy schedule. I feel proud to present my dissertation work under his guidance. I am also thankful to my panel for their encouragement and support. I thank to all the teaching and non- teaching staff of my college for providing various facilities. Last but not least I am very thankful to my friends for always helping me and my parents who have always been the driving force for all my achievements.
  • 6. e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:08/August-2021 Impact Factor- 5.354 www.irjmets.com www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [399] VI. REFERENCES [1] Ilya Levner, Hong Zhangm ,“Classification driven Watershed segmentation ”, IEEE TRANSACTIONS ON IMAGE PROCESSING VOL. 16, NO. 5, MAY 2007 [2] Austin J. Sim, Evangelia Kaza, Lisa Singer, Stephen A. Rosenberg- A review of the role of MRI in diagnosis and treatment of early stage lung cancer. [3] Ayushi Shukla, Chinmay Parab, Pratik Patil, Prof.Savita Sangam-Lung cancer detection using Image processing. [4] Cancer.net [5] https://www.researchgate.net//publication [6] Sluimer, A. Schilham, M. Prokop, and B. Van Ginneken, “Computer analysis of computed tomography scans of the lung: A survey,” IEEE Transactions on Medical Imaging, vol. 25, no. 4, pp. 385–405, 2006. [7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances In Neural Information Processing Systems, pp. 1–9, 2012.