BRAIN TUMOR DETECTION & CLASSIFICATION USING DEEP LEARNING

  • Ranganath Kulkarni, Mohammed Mouzam Imroz, Mohammed Waseem Parvez, Md Sharif Uddin, Mohammed Idrees Ahmed

Abstract

Abstract - The application of deep learning techniques for brain tumor identification and classification is thoroughly examined in this study. Our research aims to develop accurate and efficient models that can interpret medical imaging data, such as MRI and CT scans, in order to diagnose different types of brain malignancies. To this end, we use state-of-the-art machine learning techniques, particularly convolutional neural networks (CNNs) [6]. Using state-of-the-art deep learning architectures and analyzing a range of datasets, we investigate the potential of deep learning to enhance the speed and accuracy of brain tumor diagnosis. The research findings have the potential to advance medical imaging technologies and enhance the efficacy of brain tumor diagnosis and classification in clinical settings. Keywords: Brain tumor, Tumor detection, Tumor Classification, CNN, Deep Learning.

Author Biography

Ranganath Kulkarni, Mohammed Mouzam Imroz, Mohammed Waseem Parvez, Md Sharif Uddin, Mohammed Idrees Ahmed

Department of Computer Science & Engineering, Guru Nanak Dev Engineering College, Bidar, Karnataka

Visvesvaraya Technological University(VTU), Belagavi, Karnataka, India

How to Cite
Ranganath Kulkarni, Mohammed Mouzam Imroz, Mohammed Waseem Parvez, Md Sharif Uddin, Mohammed Idrees Ahmed. (1). BRAIN TUMOR DETECTION & CLASSIFICATION USING DEEP LEARNING. International Journal Of Innovation In Engineering Research & Management UGC APPROVED NO. 48708, EFI 5.89, WORLD SCINTIFIC IF 6.33, 11(8), 281-285. Retrieved from http://journal.ijierm.co.in/index.php/ijierm/article/view/2338