Deep sparse autoencoder integrated with three‐stage framework for glaucoma diagnosis

Wang, Wenle; Zhou, Wei; Ji, Jianhang; Yang, Jing; Guo, Wei; Gong, Zhaoxuan; Yi, Yugen; Wang, Jianzhong

doi:10.1002/int.22911

Cited by 6 publications

(6 citation statements)

References 51 publications

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“…We apply the SGD optimizer to train the joint learning framework for 300 epochs with a learning rate of (10e − 1-10e − 5) and add six adaptive parameters to the SGD optimizer to weigh the loss of multitask learning. [49][50][51][52], accuracy, sensitivity, specifcity, and F1-score were used to evaluate the performance of classifcation. Te accuracy rate is the ratio of the number of samples correctly classifed by the classifer to the total number of samples.…”

Section: Implementation Of Frameworkmentioning

confidence: 99%

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Zhang

Lin

Pan

et al. 2023

Computational Intelligence and Neuroscience

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Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

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Section: Implementation Of Frameworkmentioning

confidence: 99%

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Zhang

Lin

Pan

et al. 2023

Computational Intelligence and Neuroscience

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“…In their approaches, DeepLabv3+ and Fully Convolutional Networks with the residual block are employed as the segmentation networks, respectively, for segmenting the OD and OC. However, there are obvious deficiencies in OC segmentation 31 . Almubarak et al 32 regarded the Resnet‐34 model and U‐Net as the encoding layers and decoding layers, respectively, to segment the OD and OC regions, which can obtain satisfied segmentation performance with little training time.…”

Section: Related Workmentioning

confidence: 99%

“…However, there are obvious deficiencies in OC segmentation. 31 Almubarak et al 32 regarded the Resnet-34 model and U-Net as the encoding layers and decoding layers, respectively, to segment the OD and OC regions, which can obtain satisfied segmentation performance with little training time.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

Zhou

Yuhan

et al. 2022

Int J of Intelligent Sys

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Glaucoma is an eye disease that leads to irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation can effectively facilitate ophthalmologist in glaucoma diagnosis. Recently, a series of deep learning approaches attain promising performance in OD and OC segmentation but still face the challenge to precisely segment OC boundary with enhanced computational efficiency. To address this issue, we propose a novel network named Robust Multiscale Feature Extraction with Depthwise Separable Convolution (RMSDSC‐Net), which can better solve the challenging tradeoff between segmentation performance and network cost. The proposed RMSDSC‐Net is mainly composed of Multiscale Input (MSI), Depthwise Separable Convolution Unit (DSCU), Dilated Convolution Block (DCB), and External Residual Connection (ERC). First, the introduction of MSI can reduce the information loss due to the pooling layers used in the network for capturing rich feature representations. Next, to enhance segmentation performance and computational efficiency, this paper designs DSCU and DCB modules to avoid spatial information loss from minor details of the image and preserve more high‐level semantic features. Finally, this paper develops ERC established between the encoding layers and decoding layers to minimize the feature degradation problem. Hence, a high segmentation performance can be achieved using a shallow network. To evaluate the performance of the proposed network, extensive experiments have been enforced on two publicly available databases, DRISHTI‐GS and REFUGE. Our approach outperforms the state‐of‐the‐art approaches with the Dice Coefficient of (0.978, 0.919) and (0.965, 0.910) for OD and OC segmentation on DRISHTI‐GS and REFUGE databases, respectively. As a result, the proposed approach has a strong potential in analyzing fundus images for glaucoma diagnosis.

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“…With the accumulation of medical images and the development of artificial intelligence, deep learning (DL) algorithms have achieved unprecedented performance in the automatic diagnosis and lesion localization of various ophthalmic diseases, including eyelid tumors [ 7 , 8 ], keratitis [ 9 ], cataract [ 10 , 11 ], glaucoma [ 12 , 13 ], and diabetic retinopathy (DR) [ 14 , 15 ]. Among them, automatic diagnosis of eyelid tumors has received widespread attention from scholars and medical professionals due to their life-threatening potential and increasing frequency of incidence.…”

Section: Introductionmentioning

confidence: 99%

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images

Jiang,

Liu,

et al. 2024

BioMed Eng OnLine

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Background The accurate detection of eyelid tumors is essential for effective treatment, but it can be challenging due to small and unevenly distributed lesions surrounded by irrelevant noise. Moreover, early symptoms of eyelid tumors are atypical, and some categories of eyelid tumors exhibit similar color and texture features, making it difficult to distinguish between benign and malignant eyelid tumors, particularly for ophthalmologists with limited clinical experience. Methods We propose a hybrid model, HM_ADET, for automatic detection of eyelid tumors, including YOLOv7_CNFG to locate eyelid tumors and vision transformer (ViT) to classify benign and malignant eyelid tumors. First, the ConvNeXt module with an inverted bottleneck layer in the backbone of YOLOv7_CNFG is employed to prevent information loss of small eyelid tumors. Then, the flexible rectified linear unit (FReLU) is applied to capture multi-scale features such as texture, edge, and shape, thereby improving the localization accuracy of eyelid tumors. In addition, considering the geometric center and area difference between the predicted box (PB) and the ground truth box (GT), the GIoU_loss was utilized to handle cases of eyelid tumors with varying shapes and irregular boundaries. Finally, the multi-head attention (MHA) module is applied in ViT to extract discriminative features of eyelid tumors for benign and malignant classification. Results Experimental results demonstrate that the HM_ADET model achieves excellent performance in the detection of eyelid tumors. In specific, YOLOv7_CNFG outperforms YOLOv7, with AP increasing from 0.763 to 0.893 on the internal test set and from 0.647 to 0.765 on the external test set. ViT achieves AUCs of 0.945 (95% CI 0.894-0.981) and 0.915 (95% CI 0.860-0.955) for the classification of benign and malignant tumors on the internal and external test sets, respectively. Conclusions Our study provides a promising strategy for the automatic diagnosis of eyelid tumors, which could potentially improve patient outcomes and reduce healthcare costs.

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Deep sparse autoencoder integrated with three‐stage framework for glaucoma diagnosis

Cited by 6 publications

References 51 publications

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images

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