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Brain image classification, Convolutional neural network, deep learning, Brain tumour, Novel Medical Image Analysis and Detection network(MIDNet 18), VGG16
Abstract: Aim: This study aims at developing an automatic medical image analysis and detection for
accurate classification of brain tumors from MRI dataset. The study implemented our novel MIDNet18
CNN architecture in comparison with the VGG16 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 results, it could be inferred that our novel MIDNet18 was 98% better than VGG16,
which was statistically significant with p value <0.001(Independent sample t-test).
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