Abstract
Brain tumors are fatal for majority of the patients, the different nature of the tumor cells requires the use of combined medical measures, and categorizing such tumors is a difficult task for radiologists. The diagnostic structures based on PCs have been offered as an aid in diagnosing a brain tumor using magnetic resonance imaging (MRI). General functions are retrieved from the lowest layers of the neural network, and these lowest layers are responsible for capturing low-level features and patterns in the raw input data, which can be particularly unique to the raw image. To validate this, the EfficientNetB3 pre-trained model is utilized to classify three types of brain tumors: glioma, meningioma, and pituitary tumor. Initially, the characteristics of several EfficientNet modules are taken from the pre-trained EfficientNetB3 version to locate the brain tumor. Three types of brain tumor datasets are used to assess each approach. Compared to the existing deep learning models, the concatenated functions of EfficientNetB3 and genetic algorithms give better accuracy. Tensor flow 2 and Nesterov-accelerated adaptive moment estimation (Nadam) are also employed to improve the model training process by making it quicker and better. The proposed technique using CNN attains an accuracy of 99.56%, a sensitivity of 98.9%, a specificity of 98.6%, an F-score of 98.9%, a precision of 98.9%, and a recall of 99.54%.
Keywords:
deep learning, convolutional neural networks, EfficientNetB3, genetic algorithm, brain tumor classificationReferences
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