U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract

Sharma, Neha; Gupta, Sheifali; Koundal, Deepika; Alyami, Sultan; Alshahrani, Hani; Asiri, Yousef; Shaikh, Asadullah

doi:10.3390/bioengineering10010119

Cited by 35 publications

(15 citation statements)

References 29 publications

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“…Moreover, many research studies have found that EfficientNet is more effective and accurate than others. This has been observed in various fields such as medical (Zhou et al 2022 ; Sharma et al 2023 ) as well as engineering (Nguon et al 2022 ; Jin et al 2022 ).…”

Section: Resultsmentioning

confidence: 81%

“…Moreover, the U-Net architecture is famous for the application of the ready frameworks model as an encoder and a decoder(Costa et al 2021 ). Particularly EfficientNet is a convenient architecture that can be adopted as an encoder of the U-Net architecture and allows it to reach higher accuracy within the limited number of the dataset (Nguon et al 2022 ; Sharma et al 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Malikov

Cuenca

Kim

et al. 2023

J Vis

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The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones. Graphical abstract

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Section: Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Malikov

Cuenca

Kim

et al. 2023

J Vis

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“…The advantages of accurate segmentation of tiny targets and its adaptive network structure are shown by the U-Net framework, which was developed in 2015 ( 37 ). The incorporation of a U-Net as a deep learning model in diverse medical applications ( 39–44 ) has served as a prominent trigger for the motivation behind this investigation. The utilization of U-Net has been widely observed in wound segmentation.…”

Section: Discussionmentioning

confidence: 99%

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Alabdulhafith,

Ba Mahel,

Samee

et al. 2024

Front. Med.

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Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34’s deep representation learning and UNet’s efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

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“…Various techniques have been employed, including UNet with an attention mechanism [ 43 ], Levit-UNet++ [ 44 ], a combination of UNet and Mask RCNN [ 45 ], Multiview UNet [ 46 ], and an Ensemble Model [ 47 ], each achieving different levels of segmentation accuracy with Dice values ranging from 0.36 to 0.88. More recent approaches in 2023 include FPN+Efficient Net B0 [ 47 ] with a Dice coefficient of 0.8975, UNet model [ 48 ] with a Dice coefficient of 0.8854, and PSPNet+ResNet 34 [ 49 ] with a Dice coefficient of 0.8842. The UMobileNet V2 Model, featuring a MobileNetV2 encoder embedded within a UNet architecture, outperforms previous methods with a Dice coefficient of 0.8984, demonstrating promising results in segmenting gastrointestinal structures in the specified dataset.…”

Section: State-of-the-art Comparisonmentioning

confidence: 99%

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

Sharma,

Gupta,

Gupta

et al. 2024

PLoS ONE

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Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.

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U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract

Cited by 35 publications

References 29 publications

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

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