Brain Tumor Detection System using Deep Learning
Siddharth Ruria1, Priyanshu Gautam2, Aditya Raj3, Garima Pandey4
1Siddharth Ruria, Department of Computer Science and Engineering, Galgotias University, Greater Noida (Uttar Pradesh), India.
2Priyanshu Gautam, Department of Computer Science and Engineering, Galgotias University, Greater Noida (Uttar Pradesh), India.
3Aditya Raj, Department of Computer Science and Engineering, Galgotias University, Greater Noida (Uttar Pradesh), India.
4Garima Pandey, Department of Computer Science and Engineering, Galgotias University, Greater Noida (Uttar Pradesh), India.
Manuscript received on 30 June 2023 | Revised Manuscript received on 16 July 2023 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024 | PP: 23-27 | Volume-13 Issue-3, February 2024 | Retrieval Number: 100.1/ijitee.H96780712823 | DOI: 10.35940/ijitee.H9678.13030224
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This project’s objectives include locating brain tumours and enhancing patient care. Tumours are abnormal cell growths, and malignant tumours are abnormal cell growths. The two types of scans, CT and MRI frequently detect infected brain tissues. Numerous more techniques are employed for the diagnosis of brain tumours, some of which include molecular testing, and positive charges imaging of blood or lymph arteries. In order to identify disease causes like tumors, this article will use various MRI pictures. This study paper’s major goals are to 1) recognize irregular sample photos and 2) locate the tumor region. In order to administer the appropriate therapy, the aberrant portions of the photographs will anticipate the levels of tumours. From example photos, deep learning is utilized to identify anomalous areas. The aberrant section will be segmented in this study using VGG-16. The number of pixels that are malignant determines the extent of the contaminated area.
Keywords: Brain Tumor, Deep Learning, Machine Learning, MRI Scan, CT scan.
Scope of the Article: Deep Learning