The 3D tumor-Glioma segmentation is a voxel-based problem where the tumor is segmented into whole tumor with oedema, enhancing tumor, and tumor core on magnetic resonance images. In the present study, pretrained autoencoder is developed which acts as an anchor for 3D-Unet for segmenting the tumor boundaries and restoring of an image. Further, during pre-training, the loss function namely “Mean Squared Error” loss function procured a restored image. The false positive as well as false negative samples during segmentation are obtained due to consideration of improved weighted loss dice function. The learning parameters reduced to a to 9.8 M as compared to 27 M from state of art methods. This resulted in better output visually as well as in terms of other statistical parameters such as Dice similarity coefficient, specificity, sensitivity, and Hausdorff95.