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Brain tumors are among the most fatal and devastating diseases, oftenresulting in significantly reduced life expectancy. An accurate diagnosis ofbrain tumors is crucial to devise treatment plans that can extend the lives ofaffected individuals. Manually identifying and analyzing large volumes of MRIdata is both challenging and time-consuming. Consequently, there is a pressingneed for a reliable deep learning (DL) model to accurately diagnose braintumors. In this study, we propose a novel DL approach based on transferlearning to effectively classify brain tumors. Our novel method incorporatesextensive pre-processing, transfer learning architecture reconstruction, andfine-tuning. We employ several transfer learning algorithms, includingXception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments usedthe Figshare MRI brain tumor dataset, comprising 3,064 images, and achievedaccuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2,InceptionResNetV2, and DenseNet201, respectively. Our findings reveal thatResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRIbrain tumor dataset, outperforming existing models. Therefore, our proposedmodel's ability to accurately classify brain tumors in a short timeframe canaid neurologists and clinicians in making prompt and precise diagnosticdecisions for brain tumor patients.

An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning