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Title: :  Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning
PaperId: :  26272
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26272
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Y MaheswarSri Venkatesa Perumal college of Engineering and Technology
G Sai GaneshSri Venkatesa Perumal college of Engineering and Technology
A Manoj KumarSri Venkatesa Perumal college of Engineering and Technology
Avva RudheerprasadSri Venkatesa Perumal college of Engineering and Technology
G Channa malla ReddySri Venkatesa Perumal college of Engineering and Technology
C RameshSri Venkatesa Perumal college of Engineering and Technology

Abstract

Image processing
Brain tumors, Tumor segmentation, U-Net model, MRI (Magnetic Resonance Imaging), Accuracy, Deep learning, Dice similarity coefficient (DSC), Intersection over Union (IoU).
Brain tumors remain a major clinical concern due to their significant incidence and associated mortality rates, underscoring the critical need for accurate and automated segmentation to support diagnosis and treatment. While deep learning has brought notable improvements in segmentation performance, several limitations continue to challenge existing methods. In response, we propose a new architecture termed Dual Encoder Mirror Difference Residual U-Net (DEMD-ResUNet). This model utilizes two parallel encoders to process both the original and its horizontally flipped version of the input image. Furthermore, residual units replace conventional convolutional blocks within the encoder, simplifying the training process and reducing risks such as vanishing gradients or the loss of fine-grained details. To enhance feature discrimination, the architecture incorporates a Multimodal Difference Feature Augmentation (MDFA) module, which emphasizes abnormal regions across both original and mirrored modalities. In addition, a Mirror Difference Feature Fusion (MDFF) block is positioned between the encoder and decoder paths to effectively combine symmetric features from the dual encoders and improve segmentation accuracy. Experimental results, including ablation studies, validate the contribution of each proposed module. The DEMD-ResUNet achieves high Dice similarity scores on the BraTS 2018 and BraTS 2019 benchmarks, reporting 0.862, 0.925, and 0.905 for Enhanced Tumor (ET), Whole Tumor (WT), and Tumor Core (TC) respectively on BraTS 2018, and 0.869, 0.922, and 0.916 for the same metrics on BraTS 2019.

Citations

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IJARIIE Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. "Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 2646-2652
MLA Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. "Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 2646-2652.
APA Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, & C Ramesh. (2025). Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 2646-2652.
Chicago Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. "Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 2646-2652.
Oxford Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. 'Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 2646-2652. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Optimized_Pattern_Aware_Brain_Tumor_Segmentation_Using_Enhanced_U_net_Learning_ijariie26272.pdf (Accessed : ).
Harvard Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. (2025) 'Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 2646-2652IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Optimized_Pattern_Aware_Brain_Tumor_Segmentation_Using_Enhanced_U_net_Learning_ijariie26272.pdf (Accessed : )
IEEE Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh, "Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 2646-2652, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Optimized_Pattern_Aware_Brain_Tumor_Segmentation_Using_Enhanced_U_net_Learning_ijariie26272.pdf [Accessed : ].
Turabian Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. "Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver Y Maheswar, G Sai Ganesh, A Manoj Kumar, Avva Rudheerprasad, G Channa malla Reddy, and C Ramesh. Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 2646-2652. Available from: https://ijariie.com/AdminUploadPdf/Optimized_Pattern_Aware_Brain_Tumor_Segmentation_Using_Enhanced_U_net_Learning_ijariie26272.pdf
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