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Title: :  Deep Learning Based Brain Tumor Detection Using VGG16 Model
PaperId: :  20901
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20901
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
MUHAMMED FARDHEEN M MIlahia College of Engineering and Technology
BILAL MUHAMMED ASHRAFIlahia College of Engineering and Technology
AKSHARA PRAKASH Ilahia College of Engineering and Technology
AKSHAYA GIlahia College of Engineering and Technology
AZIYA SHIRIN V SIlahia College of Engineering and Technology

Abstract

Computer Science and Engineering
Visual Geometric Group 16(VGG16), Tumor Detection, Image processing, Magnetic Resonance Brain Images(MRI), Convolutional Neural Network(CNN), Image pre-processing techniques.
In the realm of brain tumor medical image processing, the segmentation of brain tumors is a vital and demanding undertaking. Relying on human-assisted manual classification for this task can lead to inaccurate predictions and diagnoses. Moreover, the difficulty is exacerbated when there is a substantial amount of data that requires assistance. In this study, we present a deep learning approach utilizing the VGG16 architecture to process 2D Magnetic Resonance brain images (MRI) and accurately distinguish between normal and abnormal cases based on texture and statistical features. By employing transfer learning techniques, we anticipate achieving an accuracy of 90\% or higher. The primary objective of image segmentation in medical image processing is the detection of tumors or lesions. Enhancing the sensitivity and specificity of tumor or lesion identification has become a central challenge in medical imaging with the aid of Computer-Aided Diagnostic (CAD) systems. However, manual segmentation of tumors or lesions is a time-consuming, challenging, and burdensome task, particularly due to the large number of MRI images generated in routine medical practice. To address this, we propose an efficient and proficient method that facilitates brain tumor segmentation and detection without the need for human assistance, leveraging the VGG16 architecture and transfer learning techniques.

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IJARIIE MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. "Deep Learning Based Brain Tumor Detection Using VGG16 Model" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 4664-4669
MLA MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. "Deep Learning Based Brain Tumor Detection Using VGG16 Model." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 4664-4669.
APA MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, & AZIYA SHIRIN V S. (2023). Deep Learning Based Brain Tumor Detection Using VGG16 Model. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 4664-4669.
Chicago MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. "Deep Learning Based Brain Tumor Detection Using VGG16 Model." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 4664-4669.
Oxford MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. 'Deep Learning Based Brain Tumor Detection Using VGG16 Model', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 4664-4669. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Deep_Learning_Based_Brain_Tumor_Detection_Using_VGG16_Model_ijariie20901.pdf (Accessed : ).
Harvard MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. (2023) 'Deep Learning Based Brain Tumor Detection Using VGG16 Model', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 4664-4669IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Deep_Learning_Based_Brain_Tumor_Detection_Using_VGG16_Model_ijariie20901.pdf (Accessed : )
IEEE MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S, "Deep Learning Based Brain Tumor Detection Using VGG16 Model," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 4664-4669, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Deep_Learning_Based_Brain_Tumor_Detection_Using_VGG16_Model_ijariie20901.pdf [Accessed : ].
Turabian MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. "Deep Learning Based Brain Tumor Detection Using VGG16 Model." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 ().
Vancouver MUHAMMED FARDHEEN M M, BILAL MUHAMMED ASHRAF, AKSHARA PRAKASH , AKSHAYA G, and AZIYA SHIRIN V S. Deep Learning Based Brain Tumor Detection Using VGG16 Model. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(3) : 4664-4669. Available from: https://ijariie.com/AdminUploadPdf/Deep_Learning_Based_Brain_Tumor_Detection_Using_VGG16_Model_ijariie20901.pdf
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