Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks

Document Type : Research Paper

Authors

1 CSE-AIML, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India.

2 Sri Ramachandra Faculty of Management Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.

3 Computing Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.

4 School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India.

5 Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.

6 Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India.

7 School of Computing Science and Artificial Intelligence, SR University, Warangal-506371, Telangana, India.

Abstract

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

Keywords

References

Ahmed, S. T., Basha, S. M., Venkatesan, M., Mathivanan, S. K., Mallik, S., Alsubaie, N., & Alqahtani, M. S. (2023). TVFx–CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique. BMC Medical Imaging, 23(1), 146.
Baskar, S., Nandhini, I., Prasad, M. L., Katale, T., Sharma, N., & Reddy, P. C. S. (2023, November). An Accurate Prediction and Diagnosis of Alzheimer’s Disease using Deep Learning. In 2023 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1-7). IEEE..
Chillakuru, P., Madiajagan, M., Prashanth, K. V., Ambala, S., Shaker Reddy, P. C., & Pavan, J. (2023). Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm. Soft Computing, 1-11.
Hossain, T., Shishir, F. S., Ashraf, M., Al Nasim, M. A., & Shah, F. M. (2019, May). Brain tumor detection using convolutional neural network. In 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT) (pp. 1-6). IEEE.
Islam Khan, M. S., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., & Dehzangi, I. (2022). Accurate brain tumor detection using deep convolutional neural network.
Kibriya, H., Amin, R., Alshehri, A. H., Masood, M., Alshamrani, S. S., & Alshehri, A. (2022). A novel and effective brain tumor classification model using deep feature fusion and famous machine learning classifiers. Computational Intelligence and Neuroscience, 2022.
Kumar, G. R., Reddy, R. V., Jayarathna, M., Pughazendi, N., Vidyullatha, S., & Reddy, P. C. S. (2023, May). Web application based Diabetes prediction using Machine Learning. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
Lokesh, S., Priya, A., Sakhare, D. T., Devi, R. M., Sahu, D. N., & Reddy, P. C. S. (2022). CNN based deep learning methods for precise analysis of cardiac arrhythmias. International journal of health sciences, 6.
Madhavi, G. B., Bhavani, A. D., Reddy, Y. S., Kiran, A., Chitra, N. T., & Reddy, P. C. S. (2023, June). Traffic Congestion Detection from Surveillance Videos using Deep Learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) (pp. 1-5). IEEE.
Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022). Multi-modal brain tumor detection using deep neural network and multiclass SVM. Medicina, 58(8), 1090.
Patil, S. B., Rao, H. R., Chatrapathy, K., Kiran, A., Kumar, A. S., & Reddy, P. C. S. (2023, August). Ensemble Deep Learning Framework for Classification of Skin Lesions. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 1550-1555). IEEE.
Ramana, A. V., Bhoga, U., Dhulipalla, R. K., Kiran, A., Chary, B. D., & Reddy, P. C. S. (2023, June). Abnormal Behavior Prediction in Elderly Persons Using Deep Learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) (pp. 1-5). IEEE.
Rao, K. R., Prasad, M. L., Kumar, G. R., Natchadalingam, R., Hussain, M. M., & Reddy, P. C. S. (2023, August). Time-Series Cryptocurrency Forecasting Using Ensemble Deep Learning. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 1446-1451). IEEE.
Raza, A., Ayub, H., Khan, J. A., Ahmad, I., S. Salama, A., Daradkeh, Y. I., ... & Hamam, H. (2022). A hybrid deep learning-based approach for brain tumor classification. Electronics, 11(7), 1146.
Rinesh, S., Maheswari, K., Arthi, B., Sherubha, P., Vijay, A., Sridhar, S., ... & Waji, Y. A. (2022). Investigations on brain tumor classification using hybrid machine learning algorithms. Journal of Healthcare Engineering, 2022.
Sampath, S., Parameswari, R., Prasad, M. L., Kumar, D. A., Hussain, M. M., & Reddy, P. C. S. (2023, December). Ensemble Nonlinear Machine Learning Model for Chronic Kidney Diseases Prediction. In 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
Sathiyamoorthi, V., Ilavarasi, A. K., Murugeswari, K., Ahmed, S. T., Devi, B. A., & Kalipindi, M. (2021). A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images. Measurement, 171, 108838.
Seetha, J., & Raja, S. S. (2018). Brain tumor classification using convolutional neural networks. Biomedical & Pharmacology Journal, 11(3), 1457.
Senan, E. M., Jadhav, M. E., Rassem, T. H., Aljaloud, A. S., Mohammed, B. A., & Al-Mekhlafi, Z. G. (2022). Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning. Computational and Mathematical Methods in Medicine, 2022.
Shah, H. A., Saeed, F., Yun, S., Park, J. H., Paul, A., & Kang, J. M. (2022). A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet. IEEE Access, 10, 65426-65438.
Shaker Reddy, P. C., & Sucharitha, Y. (2023). A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 16(5), 82-92.
Shanmugaraja, P., Bhardwaj, M., Mehbodniya, A., VALI, S., & Reddy, P. C. S. (2023). An Efficient Clustered M-path Sinkhole Attack Detection (MSAD) Algorithm for Wireless Sensor Networks. Adhoc & Sensor Wireless Networks, 55.
Shelatkar, T., & Bansal, U. (2022, March). Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach. In International Conference on Machine Intelligence and Signal Processing (pp. 105-114). Singapore: Springer Nature Singapore.
Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2022). Image segmentation for MR brain tumor detection using machine learning: A Review. IEEE Reviews in Biomedical Engineering.
Stadlbauer, A., Marhold, F., Oberndorfer, S., Heinz, G., Buchfelder, M., Kinfe, T. M., & Meyer-Bäse, A. (2022). Radiophysiomics: brain tumors classification by machine learning and physiological MRI data. Cancers, 14(10), 2363.
Sucharitha, Y., & Shaker Reddy, P. C. (2022). An autonomous adaptive enhancement method based on learning to optimize heterogeneous network selection. International Journal of Sensors Wireless Communications and Control, 12(7), 495-509.
Sucharitha, Y., Reddy, P. C. S., & Suryanarayana, G. (2023). Network Intrusion Detection of Drones Using Recurrent Neural Networks. Drone Technology: Future Trends and Practical Applications, 375-392.
Suneel, S., Balaram, A., Amina Begum, M., Umapathy, K., Reddy, P. C. S., & Talasila, V. (2024). Quantum mesh neural network model in precise image diagnosing. Optical and Quantum Electronics, 56(4), 559.
Xie, Y., Zaccagna, F., Rundo, L., Testa, C., Agati, R., Lodi, R., ... & Tonon, C. (2022). Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2021): Review, Challenges, and Future Perspectives.
Younis, A., Qiang, L., Nyatega, C. O., Adamu, M. J., & Kawuwa, H. B. (2022). Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches. Applied Sciences, 12(14), 7282.
Zahoor, M. M., Qureshi, S. A., Khan, A., Rehman, A. U., & Rafique, M. (2022). A novel dual-channel brain tumor detection system for MR images using dynamic and static features with conventional machine learning techniques. Waves in Random and Complex Media, 1-20.
Journal of Information Technology Management
Volume 16, Issue 1
2024
Pages 44-60

Files

Share

How to cite

Statistics