Dual Phase Deep Learning Network: Adaptive Canny-ResNet Fusion Brain Tumor Diagnosis System

Authors

  • Munisha Devi
  • Poonam Dhiman

Keywords:

N\A

Abstract

Brain cancer is still a major worldwide health problem, and early and precise diagnosis may make a big difference in survival rates. Traditional diagnostic approaches that depend on manual MRI analysis take a lot of time, are subjective, and are easy to make mistakes, which mean they frequently miss modest tumor borders or early-stage malignancies. To overcome these constraints, this study presents an innovative hybrid deep learning system that integrates adaptive edge detection with dual-path CNN architecture. The approach starts with preprocessing and augmentation of T1/T2/FLAIR sequences. An adaptive Canny-Sobel filter with dynamic thresholding gets rid of noise from artifacts and healthy tissues while getting high-precision tumor outlines. A ResNet-50 backbone extracts hierarchical features from these edge maps and raw scans at the same time. A spatial attention module then enhances the outlines of the tumors. The suggested system has an average F1-score of 96.7% on a Kaggle dataset including 1,311 MRI scans during five-fold cross-validation. It has very high accuracy for glioma (100%) and recall for "no tumor" (98.67%). The suggested method gives radiologists a diagnostic tool that is easy to use and works in real time, which moves cancer treatment precision forward.

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References

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Published

2025-06-17

How to Cite

1.
Devi M, Dhiman P. Dual Phase Deep Learning Network: Adaptive Canny-ResNet Fusion Brain Tumor Diagnosis System. J Neonatal Surg [Internet]. 2025Jun.17 [cited 2025Jul.10];14(32S):679-87. Available from: https://jneonatalsurg.com/index.php/jns/article/view/7431

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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