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
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Abstract: The objectives of this project 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 additional techniques are employed for the diagnosis of brain tumours, some of which include molecular testing and positron emission tomography (PET) imaging of blood or lymphatic vessels. To identify disease causes, such as tumours, this article will utilise various MRI images. This study paper’s primary objectives are to (1) identify irregular sample photos and (2) locate the tumour region. To administer the appropriate therapy, the aberrant portions of the photographs will indicate 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 the VGG-16 model. The number of malignant pixels determines the extent of the contaminated area.
Keywords: Brain Tumor, Deep Learning, Machine Learning, MRI Scan, CT scan.
Scope of the Article: Deep Learning