Dense deep transformer for medical image segmentation: DDTraMIS

In this work, DDTraMIS architecture based on vision has been designed for medical image segmentation for different medical imaging such as MRI and CT-scan. This methodology contributes novel hybrid features extracted with a bi-directional attention-based transformer encoder-decoder network along with all stage features fused with an approximation fusing algorithm. The novelty is to combine Convolution neural networks (CNNs) and shift-invariant methods to develop hybrid features. For the verification of this novel network experiment, three different datasets, such as the ACDC, LiTS, and BraTS datasets have been used. Performance analysis has been conducted using the dice similarity coefficient (DSC) and the Hausdorff distance (HD) metrics. The proposed architecture accomplished DSC values of 92.80, 96.45, 72.80, and 73.12 for ACDC, LiTS Liver, LiTS tumor, and BraTS tumor segmentation analysis, respectively. Similarly, HD metric numeric values are 8.53, 12.84, 9.69, and 10.38 for ACDC, LiTS Liver, LiTS tumor, and BraTS tumor segmentation analysis, respectively. With advantageous performance for highly features incorporated, this method learned from tiny to extensive scaling information. Comparative and quantitative analysis has proven its superior performance and effective segmenting architecture.