Ensemble-LungMaskNet: Automated Lung Segmentation using Ensembled Deep Encoders

Öksüz, Coşku; Urhan, Oğuzhan; Güllü, M. Kemal

doi:10.1109/inista52262.2021.9548367

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…In the scope of the study, we make use of our previously proposed model that is, Ensemble‐LungMaskNet 16 for lung segmentation. In this earlier work, it is demonstrated that ensembling of the pretrained encoders in different depths as a single feature extraction backbone yields superior lung segmentation performance.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This explains why we preferred to use this model in this work. In the proposed framework, the regions including lungs are first returned by the Ensemble‐LungMaskNet 16 which accepts a chest X‐ray image as an input. Then, the lung mask obtained by the network is element‐wise multiplied with the input image to obtain only the pixels within the lungs.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…1. Our previously proposed segmentation model namely the Ensemble-LungMaskNet 16 is used to segment lung regions within the X-ray images. Thus, only the region of interest (ROI) is taken into account by eliminating the irrelevant regions that are not important for classification.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this study can be summarized as follows:Our previously proposed segmentation model namely the Ensemble‐LungMaskNet 16 is used to segment lung regions within the X‐ray images. Thus, only the region of interest (ROI) is taken into account by eliminating the irrelevant regions that are not important for classification. Instead of training a CNN from scratch, a pretrained network is used as a feature extractor by finding the optimal layer for feature extraction that optimizes the classification performance. Redundant features have been eliminated to both optimize performance and reduce dimensionality.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

COVID‐19 detection with severity level analysis using the deep features, and wrapper‐based selection of ranked features

Öksüz

Urhan

Güllü

2021

Concurrency and Computation

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The SARS-COV-2 virus, which causes COVID-19 disease, continues to threaten the whole world with its mutations. Many methods developed for COVID-19 detection are validated on the data sets generally including severe forms of the disease.Since the severe forms of the disease have prominent signatures on X-ray images, the performance to be achieved is high. To slow the spread of the disease, effective computer-assisted screening tools with the ability to detect the mild and the moderate forms of the disease that do not have prominent signatures are needed. In this work, various pretrained networks, namely GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, EfficientNetB0, and Xception, are used as feature extractors for the COVID-19 detection with severity level analysis. The best feature extraction layer for each pre-trained network is determined to optimize the performance. After that, features obtained by the best layer are selected by following a wrapper-based feature selection strategy using the features ranked based on Laplacian scores. The experimental results achieved on two publicly available data sets including all the forms of COVID-19 disease reveal that the method generalized well on unseen data. Moreover, 66.67%, 90.32%, and 100% sensitivity are obtained in the detection of mild, moderate, and severe cases, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

COVID‐19 detection with severity level analysis using the deep features, and wrapper‐based selection of ranked features

Öksüz

Urhan

Güllü

2021

Concurrency and Computation

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“…image segmentation technology to facilitate detailed analysis of these areas so that the accuracy and reliability of diagnosis can be effectively improved [3].…”

Section: Introductionmentioning

confidence: 99%

HarDNet and Dual-Code Attention Mechanism Based Model for Medical Images Segmentation

Shen

Huang

2023

IEEE Access

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During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients' quality of life.

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TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model

et al. 2022

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Ensemble-LungMaskNet: Automated Lung Segmentation using Ensembled Deep Encoders

Cited by 5 publications

References 17 publications

COVID‐19 detection with severity level analysis using the deep features, and wrapper‐based selection of ranked features

COVID‐19 detection with severity level analysis using the deep features, and wrapper‐based selection of ranked features

HarDNet and Dual-Code Attention Mechanism Based Model for Medical Images Segmentation

TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model

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