Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification

Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper.

• Publication year

  2024

• Venue

  International Conference on Signal and Image Processing Applications

• Publication date

  2024-09-03

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1109/ICSIPA62061.2024.10685883
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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