Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, leading to reduced boundary accuracy, and achieve high performance primarily on highly enhanced images, making them less effective for enhancement-lagging clinical data. To address these gaps, this study proposes the Edge Incorporative Fusion (EIF) algorithm, which enhances edge-pixel contrast in MRI images, combined with the Gabor Transform (GaT) for spatial-frequency domain conversion to improve detection accuracy. These innovations integrated into a Modified Deep Learning (MDL) architecture that reduces detection time by optimizing the internal layers while maintaining superior classification performance. The EIF-MDL system developed using Python programming language and implemented in Jupyter Notebook for flexibility and reproducibility. The system was evaluated on the PLCO and NU datasets, achieving sensitivity of 98.58%, specificity of 99.09%, accuracy of 99.1%, and a Dice similarity coefficient of 98.96%, outperforming state-of-the-art methods. This robust system's ability to excel across both enhancement-lagging and highly enhanced images highlights its potential for accurate and timely glioma diagnosis, particularly in resource-constrained healthcare environments.