Brain tumor is an abnormal cell population that
occurs in the brain. Currently, medical imaging techniques
play a vital role in brain tumor diagnosis and classification.
Brain tumor classification based on Magnetic Resonance
Imaging (MRI) has become a promising research area in
the field of medical imaging systems. In the brain image, the
size of the tumor may vary from patient to patient along with
the minute details of the tumor. It is a difficult task for radiologists to diagnose and classify tumors from numerous
images. An efficient algorithm is proposed in this paper for
tumor classification based on Deep Learning (DL) models.
This paper presents three different Convolutional Neural
Network (CNN) models for the classification of brain tumors.
These models are AlexNet, VGG16, and ResNet50. As brain
images need to be stored for a long time for research and
medical causes, image compression is an efficient tool for
minimizing storage space, and also for allowing the deep
analysis of brain images. This study depends on a lossy
compression method, namely JPEG2000, for the storage of
medical brain images. Classification is applied on the dataset with and without compression to estimate the effect of
the compression method on the classification performance.
Results of the classification models show that ResNet50
achieves a 99.97% accuracy, then VGG16 reaches a 98.83%
accuracy, and finally, AlexNet gives a 92.92% accuracy
without compression. The compression process is applied
with four different compression ratios of 50, 25, 12.5, and
10%. The reduction in accuracy of classification with compression is small, as ResNet50 gives a 98.56% accuracy,
and VGG16 gives a 92.92% accuracy, while AlexNet gives
an 83.83% accuracy. |
