An efficient deep learning model to categorize brain tumor using
  reconstruction and fine-tuning
Md. Alamin Talukder Md. Manowarul Islam Md Ashraf Uddin Arnisha Akhter Md. Alamgir Jalil Pramanik Sunil Aryal Muhammad Ali Abdulllah Almoyad Khondokar Fida Hasan Mohammad Ali Moni
Brain tumors are among the most fatal and devastating diseases, often
resulting in significantly reduced life expectancy. An accurate diagnosis of
brain tumors is crucial to devise treatment plans that can extend the lives of
affected individuals. Manually identifying and analyzing large volumes of MRI
data is both challenging and time-consuming. Consequently, there is a pressing
need for a reliable deep learning (DL) model to accurately diagnose brain
tumors. In this study, we propose a novel DL approach based on transfer
learning to effectively classify brain tumors. Our novel method incorporates
extensive pre-processing, transfer learning architecture reconstruction, and
fine-tuning. We employ several transfer learning algorithms, including
Xception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments used
the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved
accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2,
InceptionResNetV2, and DenseNet201, respectively. Our findings reveal that
ResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRI
brain tumor dataset, outperforming existing models. Therefore, our proposed
model's ability to accurately classify brain tumors in a short timeframe can
aid neurologists and clinicians in making prompt and precise diagnostic
decisions for brain tumor patients.