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Brain tumor diagnosis relies on radiologist’s experience and abilities. The amount of data handled by radiologist will increases as patient increases, this causes more man power for analyzing and diagnosing patient’s data. In general, the percentage of Glioma, Meningioma, and Pituitary tumors in all brain tumors are 45%, 15%, and 15%, respectively. Deep Learning networks has revolutionized the study of brain tumor classifications. Here we used Convolutional Neural Network (CNN) and different transfer learning networks such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception. In this paper we used different categories of images and labelled them as such as glioma tumor (0), meningioma tumor (1), no tumor (2), pituitary tumor (3). Among all these networks CNN outperforms other algorithms with 92.59% accuracy.

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Classification of Brain MRI Images Using Deep Learning Networks

  • 章节

  • 发表日期：

    2026年

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  • R. Madhavan,
  • M. C. Jobin Christ,
  • R. Haripriya,
  • Nabeel Al-Milli,
  • V. Mythily,
  • P. Mohamed Sajid,
  • Jayant Giri

摘要

Brain tumor diagnosis relies on radiologist’s experience and abilities. The amount of data handled by radiologist will increases as patient increases, this causes more man power for analyzing and diagnosing patient’s data. In general, the percentage of Glioma, Meningioma, and Pituitary tumors in all brain tumors are 45%, 15%, and 15%, respectively. Deep Learning networks has revolutionized the study of brain tumor classifications. Here we used Convolutional Neural Network (CNN) and different transfer learning networks such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception. In this paper we used different categories of images and labelled them as such as glioma tumor (0), meningioma tumor (1), no tumor (2), pituitary tumor (3). Among all these networks CNN outperforms other algorithms with 92.59% accuracy.