Hybrid deep learning algorithm for brain tumour detection

ABSTRACT

Image processing fields, such as separating data from healthcare images, can benefit greatly from using deep learning algorithms. This can be extremely beneficial. For the treatment of someone with a brain tumour, discovering the tumour as soon as possible is critical. The patient's life expectancy would increase if the tumour was discovered sooner rather than later. Medical images typically lack contrast because of noise or because there aren't enough diffusive boundaries in the image. This is because of noise. In order to better understand tumours in medical image segmentation, MRI was used to diagnose the tumour, but it also played a beneficial role in clinical image analysis. Why did an MRI need to be done? This paper will examine how bad a brain tumour is using the hybrid deep learning algorithm, which is what this paper is about. Algorithms help us get the correct answer. Based on the direction of the images taken by MR, there will be a lot of information about how to divide up the pictures. In this way, three separate networks are trained to improve their segmentation results.

KEYWORDS:

• Tumour detection
• convolution neural network
• deep learning
• brain tumour
• image segmentation
• brain images
• magnetic resonance imaging
• brain tumour segmentation

Acknowledgments

The authors thank the reviewers for their efforts in improving the paper's quality.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Jyoti Srivastava

Jyoti Srivastava is a Research Scholar in the Department of ITCA at the Madan Mohan Malaviya University of Technology, Gorakhpur, India. She has published more than 5 papers in national and international conferences. Her research interests are in the domains of networks, machine learning, natural language processing and video and image processing.

Jay Prakash

Jay Prakash is an Associate Professor in the Department of ITCA at the Madan Mohan Malaviya University of Technology, Gorakhpur, India, and has a total teaching experience of 20 years. He has published more than 38 papers in national and international journals (SCI, SCIE) and conferences. His core teaching and research interests are in the domains of networks, parallel and distributed computing, architecture, and video and image processing. He conducts research in the design, analysis, and various applications of networks and distributed environments and also uses techniques from machine learning and soft computing. His research focuses on noise suppression in video and images in the transform domain, various protocols of networks, antennas in communication, and gateway discovery in Adhoc networks for Internet access. He is a reviewer of SCIE journals.

Ashish Srivastava

Ashish Srivastava is an Assistant Professor in the Department of Computer Engineering & Applications, GLA University, Mathura, India. He received his Ph.D. degree in Information Technology & Computer Application from Madan Mohan Malaviya University of Technology, Gorakhpur, India. He received his master's degree in Computer Science & Engineering (Computer Networks). He received his bachelor's degree in Information Technology. His research interests include MANET, FANET, Antenna Communication, UAV Video, image processing, and Routing protocols. He has published papers in International Journals, International Conferences, SCI, SCIE, ESCI and Scopus indexed Journal. He has been teaching since 2014 in CSE and IT departments. He is a Review Board member, Editorial Board member in various journals, and reviewer across multiple SCI, SCIE, ESCI and Scopus journals.