Title:Classification of Brain Tumours in MRI Images using a Convolutional Neural
Network
Volume: 20
Author(s): Isha Gupta, Swati Singh, Sheifali Gupta and Soumya Ranjan Nayak*
Affiliation:
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
Keywords:
Transfer learning, Brain tumour, Deep learning, Medical imaging, Classification, National brain tumor.
Abstract:
Introduction:
Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious
malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning
and image identification is CNN.
Methods:
In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan
imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16,
ResNet-50, and Inceptionv3 using the technique which is transfer learning.
Results:
Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model
accuracy achieved is 94%, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90%, whereas ResNet- 50
reached 86% and Inception v3 obtained 64% accuracy.
Conclusion:
When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly
higher accuracy outcomes and a reduction in losses.
