Figure 4-2
Conclusion:
Using a CNN network for brain tumor categorization, we evaluate the
efficacy of the network in this study. We have found that although
though a large amount of data is required to train the neural network,
we can get almost 100 percent accuracy with a medium-sized dataset,
according to our experiments.Our accuracy is better than ResNet-v2-152,
Inception-v3, and Inception-Resnet-v2. Suggested model takes 153
seconds, compared to 300 seconds for the ResNet-v2-152, 164 seconds for
the Inception-v3, and 246 seconds for the Inception-Resnet-v2. As a
result, suggested model requires fewer computing parameters due to its
shorter run time. Suggested model is significantly more accurate than
ResNet-v2-152, Inception-v3, and Inception-Resnet-v2. Our proposed
method may be predictive in diagnosing cancers in patients with brain
tumors. A detailed hyperparameter configuration and a more efficient
preprocessing strategy can be considered to improve the model’s
efficiency further. However, in future work, the proposed method could
be extended to other classification problems such as identifying brain
tumors, and it could be used to detect dangerous diseases early in other
medical cases areas and related to medical imaging, such as lung and
breast cancer, which have extremely high global mortality rates.
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