An augmented customized deep learning approach for brain tumour identification

ABSTRACT

Brain is one of the crucial organs in the human body and the survival rate for those who are affected by brain tumours across the globe is very low. There might be low survival rate yet it can be improved by identifying the disease by using MRI of the brain at a very early stage. At this juncture, the automatic identification of the tumour accurately using MRI images is very essential. Once the deep learning came into existence, the accuracy of identification of various biomedical diseases using MRI or X-ray images has been improved.Existing Systems are using deep learning approach identification of the diseases but they are lacking in the size of the data which they are using for the implementation purpose. Considering this aspect as an inspiration, a framework proposed for the identification of brain tumour with a customized neural network along with a CNN architecture. The dataset considered for the implementation of this framework is an open dataset obtained from Kaggle. As the dataset is smaller, the data augmentation technique is used to improve the dataset size. So, the effect of data augmentation in attaining the accuracy is also discussed. The training accuracy obtained during pre-data augmentation is about 85.76% and post-data augmentation is about 97.85%.

KEYWORDS:

• Brain tumour
• MRI images
• deep neural network
• data augmentation
• classification

Availability of data and materials

The code and data will be available on informed request via email to the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

All applicable institutional and/or national guidelines for the care and use of humans were followed.

Additional information

Funding

No funding received

Notes on contributors

Mangena Venu Madhavan

Venu Madhavan Mangena is a master's Student at Lovely Professional University, India. His research interest is Machine Learning, Neural Networks, Image Processing, Deep Learning, Steganography, Bio-medical Images related applications. He has published and presented more than 5 papers in Springer, Elsevier and other reputable journals which are Scopus indexed & peer-review journals.

Aditya Khamparia

Dr. Aditya Khamparia has expertise in Teaching, Entrepreneurship, and Research & Development of eight years. He is currently working as Assistant Professor and Coordinator of Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, in May 2018. He has completed his M. Tech. from VIT University and B. Tech. from RGPV, Bhopal. He has completed his PDF from UNIFOR, Brazil. He has more than 100 research papers along with book chapters including more than 15 papers in SCI indexed Journals with cumulative impact factor of above 50 to his credit. Additionally, He has authored, and edited and editing five books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest includes machine learning, deep learning, educational technologies, and computer vision.

Sagar Dhanraj Pande

Dr. Sagar Dhanraj Pande is an Assistant Professor Senior Grade at VIT-AP University, Amaravati, Andhra Pradesh, India. He received his Ph.D. in Computer Science and Engineering from a Lovely Professional University, Phagwara, Punjab, India in 2021. He received the “Young Researcher Award” and “Best Ph.D. Thesis Award” in 2022 from Universal Innovators. Also, he received the “Emerging Scientist Award” in 2021 from VDGOOD Professional Association. His research interest is Deep Learning, Machine Learning, Network Attacks, Cyber Security, and the Internet of Medical Things (IoMT). He has published and presented more than 60 papers in Springer, Elsevier, CRC, Taylor & Francis, and other reputable journals which are Scopus indexed & peer-review journals. Also, he has published papers at international conferences springer on the topics of Data Mining, Network Security, IoT, and its application. He has supervised several postgraduate students in cybersecurity, computer networks, communication, and IoT. He is responsible for teaching Artificial Intelligence, Deep Learning, Machine Learning, Cyber Crime and Security, and Python Programming courses to undergraduate and postgraduate students. He is also sharing his knowledge through his YouTube channel named sdpguruji https://www.youtube.com/c/SDPGuruji/playlists.