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Vaidhehi, V.
- Classification of Human Organ Using Image Processing
Abstract Views :248 |
PDF Views:1
Authors
Sindhu
1,
V. Vaidhehi
1
Affiliations
1 Department of Computer Sciences, Christ University, Bengaluru, IN
1 Department of Computer Sciences, Christ University, Bengaluru, IN
Source
Oriental Journal of Computer Science and Technology, Vol 10, No 2 (2017), Pagination: 333-337Abstract
The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.Keywords
Medical Imaging, Digital Images, Organs, Canny Edge Detection, K-Means Algorithm.References
- MEDICAL IMAGE PROCESSING K.M.M. Rao, V.D.P. Rao
- Canny, J., “A computational approach to edge detection”, IEEE Trans on Pattern Analysis and Machine Intelligence, 8:679-698, 1986.
- CBMIR: Shape-Based Image Retrieval Using Canny Edge Detection And K-mean Clustering Algorithm For Medical Images.B.Ramamurthy1,K.R.Chandran2
- Diagnose Breast Cancer Through Mammograms, Using Image Processing Techniques and Optimization Techniques. Dr. M. Karnan1, K. Rajiv Gandhi2.
- An Efficient k-Means Clustering Algorithm: Analysis and Implementation. Tapas Kanungo, Senior Member, IEEE, David M. Mount, Member, IEEE, Nathan S. Netanyahu, Member, IEEE, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, Senior Member, IEEE. IEEE Transactions On Pattern Analysis And Machine Intelligence, 24(7), JULY 2002.
- http://www.ijcaonline.org/icvci/number11/icvci1458.pdf
- http://www.ace.tuiasi.ro/users/103/f2_2011_7_(83-98)_Smochina.pdf
- A Review of Medical Image Classification Techniques. Smitha P. Selection grade lecturer Dept. of CSE, CE Karunagapally Shaji L. Lecture in comp.Appln Dept.of IT, CE, Karunagapally Dr. Mini M. G. Asst. Prof. & HOD Dept. of ECE, MEC Karunagapally International Conference on VLSI, Communication & Instrumentation (ICVCI) 2011 Proceedings published by International Journal of Computer Applications(IJCA)
- Thies C, Guld MO, Fischer B, Lehmann TM, “Content- based queries on the CasImage database with in the IRMA framework”, Lec Notes in Comp Sci; 3491:781-92. 2005.
- Thoma GR, Long LR, Antani SK, Biomedical Imaging research and development: knowledge from images in the medical enterprise. Technical Report Lister Hill National Ctr for Biomedical Communications, US National Library of Medicine, NIH 2006;LHNCBCTR-2006-002.
- Enhanced Elective Subject Selection for ICSE School Students using Machine Learning Algorithms
Abstract Views :191 |
PDF Views:0
Authors
Poulami Dash
1,
V. Vaidhehi
1
Affiliations
1 Department of Computer Science, Christ University, Hosur Road, Bengaluru – 560029, Karnataka, IN
1 Department of Computer Science, Christ University, Hosur Road, Bengaluru – 560029, Karnataka, IN
Source
Indian Journal of Science and Technology, Vol 10, No 21 (2017), Pagination:Abstract
Objective: Academic advising requires a lot of expertise, time and responsibility. To assist the human advisors in an efficient way, upcoming of computerized advising system is a necessity Methods/Statistical Analysis: Course Advisory System has been implemented using WEKA tool to recommend subjects for 8th class students of ICSE board. Machine learning algorithms – Naïve Bayes, J48, PART, Random Forest and KNN have been modeled and tested on the data set. The performance of each classifier has been compared and analyzed Findings: It is inferred that no advising system has been developed to assist school students in subject selection. Research work based on Indian students’ requirements is minimal. Research work based on students’ data caters more on binary class problems whereas the addressing of multi class problems is minimal. This work proposes an advising system for the school students of 8th standard of ICSE board to choose their electives. Application/Improvements: This work focuses on Indian educational system of school students. The approach takes care of the school students which will add its advantage to the existing systems. As school students are more vulnerable by taking wrong decisions, the course Advisory system will assist them in analyzing their academic history and help them choose their electives wisely. The classification algorithms might give a better accuracy with increasing instances. The Course advisory system can be enhanced using ensemble approach.Keywords
Course Advisory System, Feature Selection Algorithms, ICSE School Students, Machine Learning Algorithms, Subject Selection- An Efficient Modelling of Terrorist Groups in India using Machine Learning Algorithms
Abstract Views :208 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN
Source
Indian Journal of Science and Technology, Vol 11, No 15 (2018), Pagination:Abstract
Objectives: The study presents best machine learning models that can be employed on terrorism related data to predict the most accurate terrorist group responsible for an attack based on historic data. Methods: This paper analyses terrorism challenges faced in India by modelling the behaviour of terrorist groups using famous machine learning algorithms like J48, IBK, Naive Bayes and ensemble approach using vote. Findings: The results of the evaluation models show the accuracy percentage of various models employed and their relevance to the dataset. It was found that when the classification models are created on a data that has class imbalance problem the percentage of correctly classified instances will be very less. The paper establishes that sampling plays a main role in determining the accuracy percentage of classifier models and gives better results. The study also shows the accuracy percentage of correctly classified instances for various algorithms and shows the ideal one for the dataset. Application/Improvement: The defined model is a new approach to classify data that has major class imbalance problem by using sampling techniques like oversampling, under sampling and ensemble models.Keywords
Classification, Data Mining, Ensemble, Global Terrorism Database- An Automatic Identification of Lung Cancer from Different Types of Medical Images
Abstract Views :202 |
PDF Views:0
Authors
K. Gayathri
1,
V. Vaidhehi
1
Affiliations
1 Department of Computer Science, Christ (Deemed to be University), Hosur Road, Bangalore, IN
1 Department of Computer Science, Christ (Deemed to be University), Hosur Road, Bangalore, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 5 (2019), Pagination: 2109-2115Abstract
Identification of lung cancer from the medical images is the most difficult task. The objective of this research work is to identify the cancerous and non-cancerous lung which is taken from different medical images like Computer Tomography medical images and Positron Emission Tomography medical images. The proposed algorithm is used to predict lung cancer by using different image processing techniques. It is divided into four stages such as pre-processing, binarization, segmentation and thresholding. This research paper ensures that the image quality is retained effectively thereby extracting appropriate features for identifying cancerous and non-cancerous lung. The algorithm is trained and tested for cancerous and non-cancerous images.Keywords
Lung Cancer, Pre-Processing, Binarization, Segmentation, Thresholding, Terminalia arjuna Stem Bark, Glycyrrhiza glabra Roots, Phytochemicals.References
- Prathamesh Gawade and R.P. Chauhan, “Detection of lung cancer using image processing techniques,” International Journal of Advanced Technology and Engineering Exploration, Volume (3), Issue (1), 2016.
- Munimanda Prem Chander, M. Venkateshwara Rao, T. V. Rajinikanth, “Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study,” International Journal of Medical Imaging, Volume (5), Issue (4), 2017.
- B. Muthazhagan and T. Ravi, “An Early Diagnosis of Lung Cancer Disease Using Data Mining and Medical Image Processing Methods: A Survey,” Middle-East Journal of Scientific Research, Volume (5), Issue (4), 2016.
- Arvind Kumar Tiwari, “Prediction of Lung Cancer Using Image Processing Techniques: A Review,” An International Journal (ACII), Volume (3), Issue (1), 2016.
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- G. Niranjana, Dr. M. Ponnavaikko, “A Review on Image Processing Methods in Detecting Lung Cancer using CT Images,” International Conference on Technical Advancements in Computers and Communications, 2017.
- Shraddha G. Kulkarni, Sahebrao B. Bagal, “Lung Cancer Tumor Detection Using Image Processing and Soft Computing Techniques,” International Conference on Recent Research Development in Science, Engineering and Management, 2016.
- Neha Panpaliya, Neha Tadas, Surabhi Bobade, Rewti Aglawe, Akshay Gudadhe, “A Survey on Early Detection and Prediction of Lung Cancer,” An International Journal of Computer Science and Mobile Computing, Volume (4), Issue (1), 2015.
- Md. Badrul Alam Miah and Mohammad Abu Yousuf, “Detection of Lung Cancer form CT Image Using Image Processing and Neural Network,” International Conference on Electrical Engineering and Information Communication Technology, 2015.
- Mukesh K. Nag, Satish Patel, Rajnikant Panik, Shikha Shrivastava, Sanjay J. Daharwal, Manju R. Singh, Deependra Singh “Lung Cancer Targeting: A Review,” Research Journal of Pharmacy and Technology, Volume (6), Issue (11), 2013.
- R. Pandian, Dr. Lalitha Kumari “C T Image for Lung Cancer Identification,” Research Journal of Pharmacy and Technology, Volume (9), Issue (12), 2016.
- A.V.S.N. Murty, B.N. Jagadesh, K. Bhagavan, S. Satyanarayana “A Comparative Study of Various Edge Enhancement Filters in Spatial Domain,” Research Journal of Pharmacy and Technology, Volume (9), Issue (12), 2016.
- S. Syes Abdul Syed, T. Senthil Kumaran “FCM based Segmentation for Medical Images,” Research Journal of Pharmacy and Technology, Volume (10), Issue (12), 2017.
- Shaik Naseera “Client-Server Architecture for Embedding Patient Information on X-Ray Images,” Research Journal of Pharmacy and Technology, Volume (9), Issue (9), 2016.
- Deepak Rao Khadatkar and Yogesh Rathore “An Efficient and Useful Hybrid Approach for Detection of Lung Cancer,” Research Journal of Pharmacy and Technology, Volume (2), Issue (4), 2011.
- T. Sudhakar, Bethanney Janney. J, Haritha. D, Juliet Sahaya. M, Parvathy. V “Automatic Detection and Classification of Brain Tumor using Image Processing Techniques,” Research Journal of Pharmacy and Technology, Volume (10), Issue (11), 2017.
- Swarnakala, Natarajah Srikumaran “Brain Tumor Segmentation by EM Algorithm,” Research Journal of Pharmacy and Technology, Volume (10), Issue (9), 2017.
- Shyamala Devi M, Sruthi A. N, Saranya Jothi C “MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis,” Research Journal of Pharmacy and Technology, Volume (11), Issue (2), 2018.
- B.D. Venkatramana Reddy and T. Jayachandra Prasad “Color-Texture Image Segmentation Algorithms based on Hypercomplex Gabor Analysis,” Research Journal of Pharmacy and Technology, Volume (2), Issue (2), 2011.