  Despite the advancement in computational modeling towards brain tumor
segmentation, of which several models have been developed, it is evident from
the computational complexity of existing models which are still at an all-time
high, that performance and efficiency under clinical application scenarios are
limited. Therefore, this paper proposes a shallow encoder and decoder network
named SEDNet for brain tumor segmentation. The proposed network is adapted from
the U-Net structure. Though brain tumors do not assume complex structures like
the task the traditional U-Net was designed for, their variance in appearance,
shape, and ambiguity of boundaries makes it a compelling complex task to solve.
SEDNet architecture design is inspired by the localized nature of brain tumors
in brain images, thus consists of sufficient hierarchical convolutional blocks
in the encoding pathway capable of learning the intrinsic features of brain
tumors in brain slices, and a decoding pathway with selective skip path
sufficient for capturing miniature local-level spatial features alongside the
global-level features of brain tumor. SEDNet with the integration of the
proposed preprocessing algorithm and optimization function on the BraTS2020 set
reserved for testing achieves impressive dice and Hausdorff scores of 0.9308,
0.9451, 0.9026, and 0.7040, 1.2866, 0.7762 for non-enhancing tumor core (NTC),
peritumoral edema (ED), and enhancing tumor (ET), respectively. Furthermore,
through transfer learning with initialized SEDNet pre-trained weights, termed
SEDNetX, a performance increase is observed. The dice and Hausdorff scores
recorded are 0.9336, 0.9478, 0.9061, 0.6983, 1.2691, and 0.7711 for NTC, ED,
and ET, respectively. With about 1.3 million parameters and impressive
performance in comparison to the state-of-the-art, SEDNet(X) is shown to be
computationally efficient for real-time clinical diagnosis.
