ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

Abstract

Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images

“…Again, similar to our study, in a study using the U-net algorithm, CT images of COVID-19 patients were evaluated and the values for the sensitivity, precision, and F1-score were calculated as 0.8, 0.82 and 0.81, respectively. In the same study, it was shown that the results could be further improved by adding various modules to the U-net algorithm [35]. In another study using a fully connected network, which is a somewhat similar method, photographs of skin lesions were evaluated, and the F1-score and sensitivity values were found to be 0.912 and 0.918, respectively [36].…”

ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images

“…Before concatenation, the attention mechanism is allowed in the segmentation network. The attention mechanism in [49] , [50] , [51] combines linear transformation and non-linear activation function. The residual attention [52] extracts local and non-local features.…”

Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images

“…However, the method is conceived to segment the single class and on a small dataset. A recent approach (Raj et al, 2021) leverages a depth network, namely ADID-UNET, to enhance the COVID-19 segmentation performance on CT images. The proposed method is evaluated on public datasets and achieved a 97.01% accuracy, a precision of 87.76%, and an F 1 score of 82.00%.…”

“…Table 9 reveals the the compared performance of the proposed approach with the state-ofthe-art methods including SegNet (Budak et al, 2021), Unet (Zhou, Canu & Ruan, 2020) with attention mechanism, and ADID-UNET (Raj et al, 2021). We compared our configurations with the others on Dice, Sensitivity, and Precision values.…”

“…In recent years, deep learning algorithms have provided great tools for medical segmentation, which plays an essential role in disease diagnosis and is one of the most crucial tasks in medical image processing and analysis. Diagnostic based on segmented medical images holds an essential contribution to improving accuracy, timeliness, and disease diagnosis efficiency (Saood & Hatem, 2021;Budak et al, 2021;Zhou, Canu & Ruan, 2020;Raj et al, 2021;Yakubovskiy, 2020). For example, for ultrasound images, the physician and doctors can accurately detect and measure the size of the solid organs in the abdomen and abnormal masses on the segmented areas in the images (Ouahabi & Taleb-Ahmed, 2021;Zhou et al, 2021).…”