Brain tumor classification of magnetic resonance Images using novel CNN-based medical image Analysis and Detection network in comparison with AlexNet Brain tumor classification using novel CNN

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Ramya Mohan
Kirupa Ganapathy
Rama Arunmozhi


Brain tumor image, Binary classification, Convolutional neural network, deep learning, Novel Medical Image Analysis and Detection network (MIDNet18), AlexNet


Abstract: Aim: This research work aims in developing an automatic medical image analysis and detection for accurate classification of brain tumors from MRI dataset. The work developed a new MIDNet18 CNN architecture in comparison with the AlexNet CNN architecture for classifying normal brain images from the brain tumor images. Materials and methods: The novel MIDNet-18 CNN architecture comprises 14 convolutional layers, 7 pooling layers, 4 dense layers and 1 classification layer. The dataset used for this study has two classes: Normal Brain MR Images and Brain Tumor MR Images. This binary MRI brain dataset consists of 2918 images as training set, 1458 images as validation set and 212 images as test set. Independent sample size calculated was 7 for each group, keeping GPower at 80%. Result: From the experimental performance metrics, it could be inferred that our novel MIDNet18 achieved higher test accuracy, AUC, F1 Score, Precision and Recall over the ALEXNet algorithm. Conclusion: From the result, it could be concluded that the MIDNet18 is significantly more accurate (Independent sample t-test p <0.05) than the AlexNet in classifying the tumors from the Brain MRI images.

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