Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network

Sahil Hangaragi; Shruthi S; Uma Bharathi; Vani B N; Pooja A

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Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network

Sahil Hangaragi Shruthi S Uma Bharathi Vani B N Pooja A

Subject area: Science,Engineering and Technology · Area of research: Computer Biology

Abstract

This paper pivots on (BT) is a leading cause of mortality worldwide. Early detection and effective management are crucial in improving patient outcomes. In recent years, (DL) techniques have shown significant potential in medical image analysis. This paper investigates the use of (CNNs) for automated brain tumor detection. The study utilizes an available dataset of MRI images for training and testing a (CNN) model to classify brain scans as tumor-affected or normal. The results demonstrate that the proposed (DL)-based approach achieves high accuracy in both detection and classification, outperforming conventional methods. These findings suggest that (DL) algorithms can assist in the early diagnosis and management of brain tumors, potentially reducing the burden on healthcare systems and improving clinical decision-making.

Keywords

Deep Learning Features; MRI Images; Brain Tumor Detection; CNN Features.

References

[1] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.

[2] Zhang, Y., Dong, Z., & Phillips, P. (2019). "Deep learning in brain disease analysis: Advances and challenges." Journal of Neuroscience Methods, 325,108–120.

[3] "An Overview of Convolutional Neural Networks (CNNs)." (2023). Retrieved from TensorFlow Documentation.

[4] "MRI Brain Tumor Dataset." (2024). Retrieved from Kaggle. This web resource provides the publicly available dataset used in this study, including labeled MRI images of gliomas, meningiomas, and pituitary tumors.

[5] Patel, R., & Kumar, S. (2022). "Advancing Medical Imaging through Deep Learning: A Case Study on Brain Tumors." In Proceedings of the International Conference on Medical Image Computing and Analysis (pp. 150-157).

[6] "Implementing CNNs with Kera’s and TensorFlow." (2022). Retrieved from Kera’s Documentation.

[7] "Introduction to MRI Image Preprocessing for Deep Learning." (2023).

How to cite this paper

Sahil Hangaragi, Shruthi S, Uma Bharathi, Vani B N, Pooja A "Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 597-600

Sahil Hangaragi, Shruthi S, Uma Bharathi, Vani B N, Pooja A "Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network" Iconic Research And Engineering Journals, vol. 8, no. 9, Mar. 2025

Sahil Hangaragi, Shruthi S, Uma Bharathi, Vani B N, Pooja A (2025). Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network. Iconic Research And Engineering Journals, 8(9).

Sahil Hangaragi, Shruthi S, Uma Bharathi, Vani B N, Pooja A "Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network" Iconic Research And Engineering Journals, vol. 8, no. 9, Mar. 2025.

@article{1707410,
      author = {Sahil Hangaragi, Shruthi S, Uma Bharathi, Vani B N, Pooja A},
      title = {Deep Learning Enabled Brain Tumor Diagnosis Using Convolutional Neural Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {597-600},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707410.pdf},
      abstract = {This paper pivots on (BT) is a leading cause of mortality worldwide. Early detection and effective management are crucial in improving patient outcomes. In recent years, (DL) techniques have shown significant potential in medical image analysis. This paper investigates the use of (CNNs) for automated brain tumor detection. The study utilizes an available dataset of MRI images for training and testing a (CNN) model to classify brain scans as tumor-affected or normal. The results demonstrate that the proposed (DL)-based approach achieves high accuracy in both detection and classification, outperforming conventional methods. These findings suggest that (DL) algorithms can assist in the early diagnosis and management of brain tumors, potentially reducing the burden on healthcare systems and improving clinical decision-making.},
      keywords = {Deep Learning Features; MRI Images; Brain Tumor Detection; CNN Features.},
      month = {March},
  }