Deep Learning for Healthcare Services

Brain Tumor Detection Based on Different Deep Neural Networks - A Comparison Study

Author(s): Shrividhiya Gaikwad, Srujana Kanchisamudram Seshagiribabu, Sukruta Nagraj Kashyap, Chitrapadi Gururaj* and Induja Kanchisamudram Seshagiribabu

Pp: 63-89 (27)

DOI: 10.2174/9789815080230123020006

* (Excluding Mailing and Handling)


Glioblastoma, better known as Brain cancer, is an aggressive type of cancer that is fatal. Biomedical imaging technology now plays a prominent part in the diagnosis of cancer. Magnetic resonance imaging (MRI) is among the most efficient methods for detecting and locating brain tumors. Examining these images involves domain knowledge and is prone to human error. As computer-aided diagnosis is not widely used, this is one attempt to develop different models to detect brain tumors from the MRI image. In this chapter, we have carried out a comparison between three different architectures of Convolutional Neural Networks (CNN), VGG16, and ResNet50, and visually represented the result to the users using a GUI. Users can upload their MRI scans and check the tumor region if they have been diagnosed with cancer. Initially, pre-processed data is taken as input, and the features are extracted based on different model approaches. Lastly, the Softmax function is used for the binary classification of the tumor. To further validate the methodology, parameters like Accuracy, Recall, Precision, Sensitivity, Specificity, and f1 score are calculated. We have observed up to 86% of accuracy in the CNN model, whereas VGG16 and ResNet50 had an accuracy of 100% for our test dataset and 96% for our validation dataset.

Keywords: Bottleneck design, Brain tumor, CNN, Confusion matrix, Contouring, Data augmentation, Data pre-processing, Deep neural network, GUI, MRI images, Residual blocks, ResNet50, Transfer learning, Tumor region, User interface, Vanishing gradient, VGG16, Windows application.

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