A systematic Survey on Brain Tumor Detection using Deep Learning Techniques

  • Kavyashree H N SKSVMACET, Lakshmeshwar, Karnataka, India
  • Dr. Parashuram Barki KSVMACET, Lakshmeshwar, Karnataka, India
Keywords: brain tumor, Deep learning, neural network, classification, segmentation, feature extraction, dataset.

Abstract

Technological advancements have significantly transformed various domains, particularly the field of healthcare. The integration of advanced computational techniques in medical applications has improved the diagnosis and treatment of critical diseases such as brain tumors. Brain tumors are among the most serious and life-threatening neurological disorders, requiring accurate and early detection for effective treatment. Automated brain tumor detection systems are designed to differentiate between normal and abnormal brain tissues using medical imaging data. In recent years, medical image processing techniques, especially those based on Magnetic Resonance Imaging (MRI), have played a vital role in automating tasks such as feature extraction, segmentation, and classification. These approaches enable faster and more reliable tumor identification. A large number of studies have explored various methods for brain tumor detection using machine learning and deep learning techniques, with a primary focus on segmentation and classification tasks. This paper aims to provide a comprehensive analysis of brain tumor detection and classification methods developed between 2019 and 2025. The study evaluates widely used approaches and examines the effectiveness of Computer-Aided Diagnosis (CAD) systems in improving diagnostic performance. To ensure a broad and unbiased review, relevant research articles were collected from multiple scientific databases, including IEEE Xplore, ScienceDirect, PubMed, Google Scholar, and ResearchGate.

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Published
2026-04-19
How to Cite
H N, K., & Barki, D. P. (2026). A systematic Survey on Brain Tumor Detection using Deep Learning Techniques. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 12(1), 93-100. Retrieved from https://asianssr.org/index.php/ajct/article/view/1518
Issue
Vol 12 No 1 (2026): Volume 12 Issue I
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