What is it about?

Brain tumor is one of the deadliest diseases that affect the Central Nervous System and they should be detected earlier to avoid serious health implications. As it is one of the most dangerous types of cancer, its diagnosis is a crucial part of the healthcare sector. So through this publication, we have done a performance analysis of 19 different deep learning architectures via the method of transfer learning.

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Why is it important?

The fast diagnosis of brain tumors can save millions of lives. The development of automated systems will help the medical industry to analyze the complete information regarding the disease and thereby save the patients. Deep learning models can be used for this purpose. So here we investigated the possibility of different deep learning models.


In this paper, we have done an in-depth study of 19 different trained deep learning models like Alexnet, VGGnet, DarkNet, DenseNet, ResNet, InceptionNet, ShuffleNet, NasNet and their variants for the detection of brain tumors using deep transfer learning. The performance parameters show that NASNet-Large is outperforming others with an accuracy of 98.03% for detection and 97.87% for classification. The thresholding algorithm is used for segmenting out the tumor region if the detected output is other than normal.

Mrs Deepa P L
Mar Baselios College of Engineering and Technology

Read the Original

This page is a summary of: Performance analysis of deep transfer learning approaches in detecting and classifying brain tumor from magnetic resonance images, Intelligent Data Analysis, November 2023, IOS Press,
DOI: 10.3233/ida-227321.
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