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Towards Design of Brain Tumor Detection Framework Using Deep Transfer Learning Techniques
Abstract
Brain tumor has surpassed all other types of cancers as it is the most diagnosed malignancy worldwide, and it is also the leading cause of death. Early detection and diagnosis of a brain tumor allow doctors to give better therapy and a higher chance for the patient's life. Recently, many strategies that leverage machine learning and deep learning models for detection and categorization have been presented. This chapter focuses on the design of a novel brain tumor detection and classification framework using well-known deep transfer learning models such as DenseNet201, DenseNet169, DenseNet121, MobileNet_v2, VGG19, VGG16, and Xception. Performance evaluation of the proposed framework is evaluated using a benchmark dataset in terms of accuracy and loss. It is observed that with DenseNet201, a training accuracy of 97.49% and a validation accuracy of 96.43% are observed. However, for MobileNet v2, Densenet169, and Xception model, 96% accuracy is observed. As a result, it is observed that the DenseNet201 model outperformed all other models in terms of accuracy.
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