Multiclass Brain Tumor Detection and Classification Using Deep Learning Techniques

Authors

  • Amusha. K  M.E(Applied Electronics), Dept of ECE, LITES, Thovalai, India
  • Mr. Vino Ruban Singh  Project Guide Dept of ECE, LITES, Thovalai, India
  • Dr. Ferlin Deva Shahila  Head of Department, Dept of ECE, LITES, Thovalai, India

Keywords:

Convolutional Neural Networks, CT Brain, Brain Tumor

Abstract

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using CT brain images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. CNN classifier used to compare the trained and test data, from this we can get the classified result for tumor. The experimental results of proposed technique have been evaluated and validated for classification performance on magnetic resonance brain images, based on accuracy, sensitivity, and specificity. Detection, extraction and classification of tumor from CT brain images of the brain is done by using Python.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-08-30

Issue

Volume 9, Issue 4, July-August-2022

Section

Research Articles

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How to Cite

[1]
Amusha. K, Mr. Vino Ruban Singh, Dr. Ferlin Deva Shahila "Multiclass Brain Tumor Detection and Classification Using Deep Learning Techniques" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 4, pp.439-445, July-August-2022.