An Optimized Deep Learning Model with Feature Fusion for Brain Tumor Detection


Suraj Patil
Dnyaneshwar Kirange


The automatic detection of brain tumor from large volumes of MRI images using deep learning is a issue that necessitates substantial computing resources. So,in this study, a brain tumor detection framework using feature fusion from optimized shallow and deep learning models is proposed that efficiently detects different types of tumors. The human brain is a 3D object and the intensity of abnormal tissue varies as per location. As a result, incorporating surrounding tissue into tumor region can help to discriminate between the type of tumor and its severity. To extract deep characteristics from tumor region and adjacent tissues, deep models such as Inception-V3 is employed using transfer learning. Deep features are especially important in tumour detection, however as the network size grows, certain low-level insights about tumor are lost. As a result, a novel optimized shallow model is designed to extract low-level features. To overcome this limitation of information loss, deep and shallow features are fused. Our extensive simulation and experiment done on a publicly available benchmark dataset shows that an optimized hybrid deep learning model with ROI expansion improves tumor detection accuracy by 9\%. These findings support the theory that the tissues adjacent to the tumor contain unique information and feature fusion compensates for information loss.


How to Cite
Patil, S., & Dnyaneshwar Kirange. (2023). An Optimized Deep Learning Model with Feature Fusion for Brain Tumor Detection. International Journal of Next-Generation Computing, 14(1).


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