Tuberculosis is a bacterial infection that usually affects the lungs; however, it can be treated if diagnosed earlier. Effective analysis of computed tomography images of lungs captured from tuberculosis patients is one of the effective approaches to determining the disease's severity. Previous medical computed tomography image analysis algorithms have suffered many setbacks during the segmentation process. In this study, a novel approach to image segmentation is proposed. DAvoU-Net model comprises Multi-Scale Residual Block and Receptive Dense Connection to segment tuberculosis-affected areas from original CT images. The 2-dimensional weight parameters of seven different deep pre-trained neural networks were first converted into 3-dimensional weights using a rotation operation before being used to train a new 3-dimensional convolutional neural network. The extracted image features from the 3-dimensional convolutional neural network serve as input into a bi-directional long short-time memory for extracting high-level discriminative features. The proposed DAvoU-Net model outperformed the original U-Net model in segmenting ImageCLEF 2019 tuberculosis dataset. It generated a dice score of 98.34 % compared to 84.21 % produced by the original U-Net. Overall, the combination of DAvoU-Net + ResNet-50 along with a 3-dimensional convolutional neural network and bi-directional long short-time memory performed better than the six other deep pre-trained neural networks. It produced an overall Area Under the ROC Curve (AUC) and a Classification Accuracy (ACC) of 0.8513 and 0.8119 respectively.