Abstract
From the past decade, many researchers are focused on the brain tumor detection mechanism using magnetic resonance images. The traditional approaches follow the feature extraction process from bottom layer in the network. This scenario is not suitable to the medical images. To address this issue, the proposed model employed Inception-v3 convolution neural network model which is a deep learning mechanism. This model extracts the multi-level features and classifies them to find the early detection of brain tumor. The proposed model uses the deep learning approach and hyper parameters. These parameters are optimized using the Adam Optimizer and loss function. The loss function helps the machines to model the algorithm with input data. The softmax classifier is used in the proposed model to classify the images in to multiple classes. It is observed that the accuracy of the Inception-v3 algorithm is recorded as 99.34% in training data and 89% accuracy at validation data.
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Lakshmi, M.J., Nagaraja Rao, S. Brain tumor magnetic resonance image classification: a deep learning approach. Soft Comput 26, 6245–6253 (2022). https://doi.org/10.1007/s00500-022-07163-z
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DOI: https://doi.org/10.1007/s00500-022-07163-z