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January 14, 2026Premier journal of science.0 citationsOpen Access

Brain Tumor Detection and Classification Using MRI and CT Image Fusion

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JSJonnalagadda Cynthia SmileyRPRajdev PreselvanKVKakani Vamsidhar

Key Points

  • The research aims to enhance brain tumor detection and classification using advanced MRI techniques.
  • Developed a customized convolutional neural network (CNN) model for predicting brain tumors.
  • Compared the CNN model against existing models for effectiveness.
  • Evaluated model efficiency using accuracy, precision, F1 score, and recall.
  • Custom CNN model achieved higher classification accuracy than existing models.
  • F1 score improved significantly with the use of MRIs for tumor classification.
  • MRI provided valuable information on tumor location.

Abstract

Brain Tumors are contributing significantly to the global mortality rates. These brain tumors must be detected early and should be classified accurately. Magnetic Resonance Imaging (MRI) is the most commonly used imaging technique. It plays a key role in accurately classifying brain tumors. MRI provides high soft tissue contrast and gives information about tumor location, size and shape. This study proposes an MRI technique for brain tumor classification. In this study, we have developed a customized convolutional neural network (CNN) model for predicting brain tumors. The customized CNN model that classifies the type of tumor. The customized CNN is compared to other existing models to derive our conclusion. The model’s efficiency is evaluated using accuracy, precision, F1 score, and recall.

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Cite This Study

Smiley et al. (2026) studied this question.

synapsesocial.com/papers/6966f32713bf7a6f02c00f5dhttps://doi.org/10.70389/pjs.100229
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