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Xiao P, Pan Y, Cai F, Tu H, Liu J, Yang X, Liang H, Zou X, Yang L, Duan J, Xv L, Feng L, Liu Z, Qian Y, Meng Y, Du J, Mei X, Lou T, Yin X, Tan Z. A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis. Front Physiol 2022; 13:1060591. [PMID: 36467700 PMCID: PMC9716070 DOI: 10.3389/fphys.2022.1060591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/07/2022] [Indexed: 07/30/2023] Open
Abstract
Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem. Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy. Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors' experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.
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Affiliation(s)
- Ping Xiao
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
- Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen Children’s Hospital, Shenzhen, China
| | - Yuhang Pan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Feiyue Cai
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
- Shenzhen Nanshan District General Practice Alliance, Shenzhen, China
| | - Haoran Tu
- Group International Division, Shenzhen Senior High School, Shenzhen, China
| | - Junru Liu
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Xuemei Yang
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Huanling Liang
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Xueqing Zou
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Li Yang
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Jueni Duan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Long Xv
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Lijuan Feng
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Zhenyu Liu
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Yun Qian
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Yu Meng
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Jingfeng Du
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Xi Mei
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Ting Lou
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Xiaoxv Yin
- School of Public Health, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Tan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
- Shenzhen Nanshan District General Practice Alliance, Shenzhen, China
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Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1770531. [PMID: 36238476 PMCID: PMC9553330 DOI: 10.1155/2022/1770531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/11/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022]
Abstract
Background and Objective. Breast cancer is a common malignant tumor that seriously threatens the health of women in my country and even around the world. The proliferation marker Ki-67 has been utilized to distinguish luminal B from luminal A tumors and is a reliable indicator of more aggressive breast cancer growth. If a reliable prediction method for breast cancer patients to avoid invasive damage can be found to predict Ki-67 before pathological examination, it will be very beneficial for doctors to formulate later treatment plans and provide more useful treatment options. Methodology. This paper proposes a tumor segmentation and prediction framework based on the combination of improved attention U-Net and SVM. The framework first improves on attention U-Net by introducing coefficients for learning multidimensional attention. Make the attention mechanism more aware of the main situation in the segmentation process. At the same time, the segmented breast MRI results and corresponding labels were input into the SVM classifier to accurately predict the expression of Ki-67. Results. The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion. Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.
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Construction of Prediction Model of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6991218. [PMID: 35651924 PMCID: PMC9150995 DOI: 10.1155/2022/6991218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 05/08/2022] [Accepted: 05/10/2022] [Indexed: 12/22/2022]
Abstract
Objective The children with Henoch-Schönlein purpura (HSP) may suffer from renal insufficiency, which seriously affects the life and health of the children. This study aims to construct a prediction model of Henoch-Schönlein purpura nephritis (HSPN). Methods A total of 240 children with HSP treated in dermatology and pediatrics in our hospital were selected. The general information, patients' clinical symptoms, and laboratory examination indicators were collected for feature selection, and the XGBoost algorithm prediction model was built. Results According to the input feature indexes, the top ten crucial feature indicators output by the XGBoost model were urine N-acetyl-β-D-aminoglucosidase, urinary retinol-binding protein, IgA, age, recurrence of purpura, purpura area, abdominal pain, 24-h urinary protein quantification, percentage of neutrophils, and serum albumin. The areas under the curves of the training set (0.895, 95% CI: 0.827-0.963) and test set (0.870, 95% CI: 0.799-0.941) models were similar. Conclusion The prediction model based on XGBoost is used to predict HSP renal damage based on clinical data of children, which can reduce the harm caused by invasive examination for patients.
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Chen GH, Ni J, Chen Z, Huang H, Sun YL, Ip WH, Yung KL. Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features. SENSORS 2022; 22:s22072455. [PMID: 35408070 PMCID: PMC9002920 DOI: 10.3390/s22072455] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 11/16/2022]
Abstract
Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time and to apply the detection methods in common scenarios, the CNN structure with preprocessed image data needs to be simplified. In this work, a detection method based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG) features is proposed. First, the Gamma correction was employed to highlight the grayscale distribution of the damage area, which compresses the space of normal pavement. The preprocessed image was then divided into several unit cells, whose grayscale and HOG were calculated, respectively. The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG (GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure and parameters. The trained indices suggested that the performance of the GHOG-based method was significantly improved, compared with the traditional HOG-based method. Furthermore, the GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy, in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale has a definite physical meaning, the present detection method possesses a potential application for the further detection of damage details in the future.
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Affiliation(s)
- Guo-Hong Chen
- School of Information and Electrical Engineering, Zhejiang University City College, 51 Huzhou Street, Hangzhou 310015, China; (G.-H.C.); (J.N.); (Z.C.)
| | - Jie Ni
- School of Information and Electrical Engineering, Zhejiang University City College, 51 Huzhou Street, Hangzhou 310015, China; (G.-H.C.); (J.N.); (Z.C.)
| | - Zhuo Chen
- School of Information and Electrical Engineering, Zhejiang University City College, 51 Huzhou Street, Hangzhou 310015, China; (G.-H.C.); (J.N.); (Z.C.)
| | - Hao Huang
- Hubei Key Laboratory of Ferro- & Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, 368 Youyi Street, Wuhan 430062, China
- Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
- Correspondence: (H.H.); (Y.-L.S.)
| | - Yun-Lei Sun
- School of Information and Electrical Engineering, Zhejiang University City College, 51 Huzhou Street, Hangzhou 310015, China; (G.-H.C.); (J.N.); (Z.C.)
- Correspondence: (H.H.); (Y.-L.S.)
| | - Wai Hung Ip
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; (W.H.I.); (K.L.Y.)
| | - Kai Leung Yung
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; (W.H.I.); (K.L.Y.)
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Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4312117. [PMID: 35047054 PMCID: PMC8763489 DOI: 10.1155/2022/4312117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 11/17/2022]
Abstract
Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that
years, length of
d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was
, which was less than that of the ANN model (
). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.
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Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area. REMOTE SENSING 2019. [DOI: 10.3390/rs11080980] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.
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