Deep CNN based brain tumor detection in intelligent systems
The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | International Journal of Intelligent Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603023000465 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846157783393632256 |
|---|---|
| author | Brij B. Gupta Akshat Gaurav Varsha Arya |
| author_facet | Brij B. Gupta Akshat Gaurav Varsha Arya |
| author_sort | Brij B. Gupta |
| collection | DOAJ |
| description | The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. |
| format | Article |
| id | doaj-art-442ca7ac7521439db7fad5bffe6cde8e |
| institution | Kabale University |
| issn | 2666-6030 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj-art-442ca7ac7521439db7fad5bffe6cde8e2024-11-25T04:41:49ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302024-01-0153037Deep CNN based brain tumor detection in intelligent systemsBrij B. Gupta0Akshat Gaurav1Varsha Arya2International Center for AI and Cyber Security Research and Innovations, & Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, ChinaRonin Institute, Montclair, NJ, USA; Corresponding author.International Center for AI and Cyber Security Research and Innovations, & Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, China; Ronin Institute, Montclair, NJ, USA; Department of Electrical and Computer Engineering, Lebanese American University, Beirut, 1102, Lebanon; Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India; School of Computing, Skyline University College, P.O. Box 1797, Sharjah, United Arab Emirates; Department of Business Administration, Asia University, Taiwan, China; Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, IndiaThe early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications.http://www.sciencedirect.com/science/article/pii/S2666603023000465Brain tumor detectionDeep learningConvolutional neural network (CNN)Medical Imaging, Industrial information systems |
| spellingShingle | Brij B. Gupta Akshat Gaurav Varsha Arya Deep CNN based brain tumor detection in intelligent systems International Journal of Intelligent Networks Brain tumor detection Deep learning Convolutional neural network (CNN) Medical Imaging, Industrial information systems |
| title | Deep CNN based brain tumor detection in intelligent systems |
| title_full | Deep CNN based brain tumor detection in intelligent systems |
| title_fullStr | Deep CNN based brain tumor detection in intelligent systems |
| title_full_unstemmed | Deep CNN based brain tumor detection in intelligent systems |
| title_short | Deep CNN based brain tumor detection in intelligent systems |
| title_sort | deep cnn based brain tumor detection in intelligent systems |
| topic | Brain tumor detection Deep learning Convolutional neural network (CNN) Medical Imaging, Industrial information systems |
| url | http://www.sciencedirect.com/science/article/pii/S2666603023000465 |
| work_keys_str_mv | AT brijbgupta deepcnnbasedbraintumordetectioninintelligentsystems AT akshatgaurav deepcnnbasedbraintumordetectioninintelligentsystems AT varshaarya deepcnnbasedbraintumordetectioninintelligentsystems |