Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification

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Year : 2026 | Volume : 04 | 01 | Page :
    By

    Sanjana Gupta,

  • Vanshika Garg,

  • Ujjwal Sahu,

  • Dilip Kumar Bharti,

  1. , Department of Computer Science and Engineering (Data Science) ABES Engineering College,, Uttar Pradesh, India
  2. , Department of Computer Science and Engineering (Data Science) ABES Engineering College, Ghaziabad,, Uttar Pradesh, India
  3. , Department of Computer Science and Engineering (Data Science) ABES Engineering College, Ghaziabad,, Uttar Pradesh, India
  4. , ABES Engineering College, Ghaziabad, Uttar Pradesh, India

Abstract

Brain tumors have become a leading cause of cancer- related deaths, posing significant health risks to many patients. This urgent medical challenge calls for rapid, automated, and reliable techniques to detect brain tumors accurately. Timely and precise tumor identification is crucial for devising effective medical plans that have the potential to save lives and improve patient outcomes. By leveraging advanced image processing methods, healthcare professionals can enhance their diagnostic capabilities and provide targeted treatments. A tumor is characterized by the abnormal and unchecked proliferation of cells. In the brain, such growth depletes vital nutrients meant for healthy tissues, ultimately impairing brain function. Currently, tumor detection often involves manual analysis of MRI scans by medical experts, a process that is both labor- intensive and susceptible to human error. Tumors, defined as masses of abnormally growing tissue, disrupt the normal functioning of the body. To overcome these challenges, deep learning like neural networks (CNN) and transfer learning models, including VGG-16 (Visual Geometry Group), can be utilized. These methods aim to identify the presence of tumors in brain images with high accuracy. The system provides a straightforward output—indicating ”yes” if a tumor is detected and ”no” otherwise—thereby streamlining the diagnostic process and enhancing reliability.

Keywords: brain tumors, cancer, CNN, MRI, VGG-16

How to cite this article:
Sanjana Gupta, Vanshika Garg, Ujjwal Sahu, Dilip Kumar Bharti. Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification. International Journal of Cell Biology and Cellular Functions. 2026; 04(01):-.
How to cite this URL:
Sanjana Gupta, Vanshika Garg, Ujjwal Sahu, Dilip Kumar Bharti. Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification. International Journal of Cell Biology and Cellular Functions. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijcbcf/article=2026/view=236082


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Ahead of Print Subscription Review Article
Volume 04
01
Received 14/07/2025
Accepted 28/08/2025
Published 17/01/2026
Publication Time 187 Days
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