An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun, Hao; Wang, Wenhui; Liu, Chen

doi:10.3390/s21124182

Cited by 37 publications

(17 citation statements)

References 65 publications

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“…Transfer learning allows for pretraining on a very large dataset of images and then tuning the resulting model using specific samples, which is useful for classification to train a stable, unbiased, and non-overfitting deep learning architecture from the very beginning [ 15 ]. Inception and ResNet models are the most frequently used for medical studies, such as the Inception V3 model, which performed well in pathological classification of NSCLC [ 16 ], breast cancer [ 17 ], and knee injury for MRI images [ 18 ] and the ResNet50 model, which is widely applied in diagnosis o9f brain diseases [ 19 ], classification of skin lesions [ 20 ], and coronary artery calcium detection [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Wang

Zhou

et al. 2022

Med Sci Monit

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Background In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to distinguish between malignant and benign solid lung nodules. Material/Methods CT images from 202 patients with 210 lesions (malignant: 127, benign: 83) manifesting as solid lung nodules from January 2016 to December 2020 from 3 institutions were retrospectively collected, and each nodule was histopathologically confirmed. Two experienced thoracic radiologists reviewed all images and determined the regions of interest (ROIs) in the three-dimensional (3D) images layer-by-layer. We divided the lesions and images into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training dataset. Five-fold cross-validation was used to choose the optimal model. Receiver operator characteristic curves (ROC curves) for methods to evaluate the performance of the models were drafted. Results In the validation set, the AUC, accuracy, sensitivity, and specificity of Inception V3 model (lesion-level) were 0.999, 0.989, 0.983, and 1.0, respectively, which is higher than the image-level (0.997, 0.933, 0.922, and 0.948, respectively). The Inception V3 model (lesion-level) performed better than the image-level but there was no significant difference between the models ( P >0.05). The ResNet50 model based on image level achieved AUC, accuracy, sensitivity, and specificity of 0.963, 0.926, 0.916, and 0.944, respectively, which is lower than that of Inception V3. Conclusions Our study developed a novel deep learning model based on noncontrast and thin-layer CT scans to classify benign vs malignant lung nodules, and the Inception V3 model greatly improved the differentiation accuracy and specificity.

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Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Wang

Zhou

et al. 2022

Med Sci Monit

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“…Finally, we used ResNet (skip connection via addition) to backpropagate through the identity function, just by vector addition [ 12 ]. The gradient was simply multiplied by one and its value was maintained in the earlier layers.…”

Section: Methodsmentioning

confidence: 99%

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring

et al. 2022

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Background: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. Methods: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. Results: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. Conclusions: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

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“…e structure characteristics of several common residual neural networks are shown as follows [16][17][18] 4…”

Section: Basis Of Residual Neural Networkmentioning

confidence: 99%

Leisure Sports Behavior Recognition Algorithm Based on Deep Residual Network

Chou

2022

Scientific Programming

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Aiming at the related problems existing in the field of leisure sports computing, in order to study the behavior recognition of leisure sports by deep residual network, based on the deep residual neural network theory, the behavior recognition algorithm and the corresponding robust model are used to analyze the leisure sports related samples, and the correlation model is used to predict and analyze the leisure sports related content. The results show that the change curves of Sig and Tanh functions can be divided into slow increasing stage, linear increasing stage, and stable stage. The y value corresponding to ReLU curve shows a linear change trend with the increase of x value. The Leaky function’s corresponding curve can be divided into two stages. The function coincides with the ReLU function in the first quadrant and remains linear in the third quadrant. The activation function curves corresponding to layers 56 and 20 have a relatively large variation range, and both of them show an overall trend of gradual decline. On the whole, the curve value corresponding to layer 56 is higher than that corresponding to layer 20, indicating that the method of layer 20 is relatively good and the corresponding training error is relatively low. It can be seen from the robustness recognition rate of various methods under different training samples that Fl has the highest overall data recognition rate while Sc has relatively poor stability. However, the recognition rate of IDCC and DCC shows a relatively flat trend, indicating that these two methods have certain advantages in describing the robust recognition rate. The research results can provide theoretical support for the application of deep residual neural networks in other fields.

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An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Cited by 37 publications

References 65 publications

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring

Leisure Sports Behavior Recognition Algorithm Based on Deep Residual Network

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