Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Hsu, Chih‐Yu; Hu, Rong; Xiang, Yunjie; Long, Xionghui; Li, Zuoyong

doi:10.3390/math10152597

Cited by 9 publications

(6 citation statements)

References 43 publications

(62 reference statements)

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“…Cross-entropy loss [ 28 ]) (CE loss): It quantifies the disparity between the predicted value and the actual value on a per-pixel basis, considering all pixels within the image equally. It belongs to global loss.…”

Section: Methodsmentioning

confidence: 99%

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation

Li,

Zhu,

Zhao

et al. 2024

Bioengineering

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This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of “network structure” and “loss function”, and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of “nnUNet” network structure and “Cross-Entropy + Iou + Boundary” hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services.

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Section: Methodsmentioning

confidence: 99%

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation

Li,

Zhu,

Zhao

et al. 2024

Bioengineering

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“…These techniques enable the model to effectively capture both global and local context. DeepLabv3+ integrates, which allows it to efficiently gather contextual information at several scales [22,23]. ASPP improves the model's capacity to detect items of different sizes and scales in an image by employing atrous (dilated) convolutions at many levels.…”

Section: Segmentationmentioning

confidence: 99%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

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“…The DeepLabv3+ model [43,44] is a variant of a typical fully convolutional neural network that has achieved good performance in using contextual information for semantic segmentation. In this paper, we propose an improved DeepLabv3+ network architecture, called IDLN [8], which is shown in Figure 4. The IDLN uses the Atrous Spatial Pyramid Pooling (ASPP) module [45] to capture contextual semantic features at different scales by using parallel hole convolution techniques with different expansion rates and retains the DeepLabv3+ model encoding-decoding structure.…”

Section: Eye Semantic Segmentation Modelmentioning

confidence: 99%

“…The performance of the eye segmentation model affects the effectiveness of our overall face occlusion automatic fatigue-driving detection method. We use the IDLN model to segment the eye region, which is trained on EIMDSD [8]. The experimental results are shown in Table 1.…”

Section: Eye Semantic Segmentationmentioning

confidence: 99%

“…We then use an improved DeepLabv3+ network (IDLN) architecture to segment the eye region. The natural light face dataset (Eye Image Detection and Segmentation Dataset, EIMDSD) [8] and the infrared light image dataset (CASIA-Iris-Distance infrared face image dataset) [9] are employed to verify the effectiveness of our method during both day and night conditions. The results show that our method has a good segmentation effect in both conditions.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Weighted Eye State Determination for Driving Fatigue Detection

Xiang

et al. 2023

Mathematics

Self Cite

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Fatigue is a significant cause of traffic accidents. Developing a method for determining driver fatigue level by the state of the driver’s eye is a problem that requires a solution, especially when the driver is wearing a mask. Based on previous work, this paper proposes an improved DeepLabv3+ network architecture (IDLN) to detect eye segmentation. A Gaussian-weighted Eye State Fatigue Determination method (GESFD) was designed based on eye pixel distribution. An EFSD (Eye-based Fatigue State Dataset) was constructed to verify the effectiveness of this algorithm. The experimental results showed that the method can detect a fatigue state at 33.5 frames-per-second (FPS), with an accuracy of 94.4%. When this method is compared to other state-of-the-art methods using the YawDD dataset, the accuracy rate is improved from 93% to 97.5%. We also performed separate validations on natural light and infrared face image datasets; these validations revealed the superior performance of our method during both day and night conditions.

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Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Cited by 9 publications

References 43 publications

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Gaussian Weighted Eye State Determination for Driving Fatigue Detection

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