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Demirbaş AA, Üzen H, Fırat H. Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. Health Inf Sci Syst 2024; 12:32. [PMID: 38685985 PMCID: PMC11056348 DOI: 10.1007/s13755-024-00290-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.
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Affiliation(s)
| | - Hüseyin Üzen
- Department of Computer Engineering, Faculty of Engineering, Bingol University, Bingol, Turkey
| | - Hüseyin Fırat
- Department of Computer Engineering, Faculty of Engineering, Dicle University, Diyarbakır, Turkey
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2
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K P AG, D RR, N MS, P LB. Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model. Technol Health Care 2024:THC240603. [PMID: 39031411 DOI: 10.3233/thc-240603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
BACKGROUND Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field. OBJECTIVE Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances. METHODS In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features. RESULTS The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness. CONCLUSIONS The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.
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Affiliation(s)
- Ajitha Gladis K P
- Department of Information Technology, CSI Institute of Technology, Thovalai, India
| | - Roja Ramani D
- Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, India
| | - Mohana Suganthi N
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
| | - Linu Babu P
- Department of Electronics and Communication Engineering, IES College of Engineering, Thrissur, India
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3
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Levenson RM, Singh Y, Rieck B, Hathaway QA, Farrelly C, Rozenblit J, Prasanna P, Erickson B, Choudhary A, Carlsson G, Sarkar D. Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology. J Transl Med 2024; 104:102060. [PMID: 38626875 DOI: 10.1016/j.labinv.2024.102060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024] Open
Abstract
Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.
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Affiliation(s)
- Richard M Levenson
- Department of Pathology and Laboratory Medicine, University of California Davis, Davis, California.
| | - Yashbir Singh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota.
| | - Bastian Rieck
- Helmholtz Munich and Technical University of Munich, Munich, Germany
| | - Quincy A Hathaway
- Department of Medical Education, West Virginia University, Morgantown, West Virginia
| | | | | | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | | | | | - Gunnar Carlsson
- Department of Mathematics, Stanford University, Stanford, California
| | - Deepa Sarkar
- Institute of Genomic Health, Ichan school of Medicine, Mount Sinai, New York
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Zhou J, Song W, Liu Y, Yuan X. An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning. PeerJ Comput Sci 2024; 10:e2059. [PMID: 38855223 PMCID: PMC11157572 DOI: 10.7717/peerj-cs.2059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 04/23/2024] [Indexed: 06/11/2024]
Abstract
Diagnosing gastrointestinal (GI) disorders, which affect parts of the digestive system such as the stomach and intestines, can be difficult even for experienced gastroenterologists due to the variety of ways these conditions present. Early diagnosis is critical for successful treatment, but the review process is time-consuming and labor-intensive. Computer-aided diagnostic (CAD) methods provide a solution by automating diagnosis, saving time, reducing workload, and lowering the likelihood of missing critical signs. In recent years, machine learning and deep learning approaches have been used to develop many CAD systems to address this issue. However, existing systems need to be improved for better safety and reliability on larger datasets before they can be used in medical diagnostics. In our study, we developed an effective CAD system for classifying eight types of GI images by combining transfer learning with an attention mechanism. Our experimental results show that ConvNeXt is an effective pre-trained network for feature extraction, and ConvNeXt+Attention (our proposed method) is a robust CAD system that outperforms other cutting-edge approaches. Our proposed method had an area under the receiver operating characteristic curve of 0.9997 and an area under the precision-recall curve of 0.9973, indicating excellent performance. The conclusion regarding the effectiveness of the system was also supported by the values of other evaluation metrics.
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Affiliation(s)
- Jiajie Zhou
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Wei Song
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Yeliu Liu
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Xiaoming Yuan
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
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Sharma N, Gupta S, Gupta D, Gupta P, Juneja S, Shah A, Shaikh A. UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans. PLoS One 2024; 19:e0302880. [PMID: 38718092 PMCID: PMC11078421 DOI: 10.1371/journal.pone.0302880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 04/14/2024] [Indexed: 05/12/2024] Open
Abstract
Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.
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Affiliation(s)
- Neha Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Punit Gupta
- University College Dublin, Dublin, Ireland
- Manipal University Jaipur, Jaipur, India
| | - Sapna Juneja
- International Islamic University, Kuala Lumpur, Malaysia
| | - Asadullah Shah
- International Islamic University, Kuala Lumpur, Malaysia
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Siddiqui S, Akram T, Ashraf I, Raza M, Khan MA, Damaševičius R. CG‐Net: A novel CNN framework for gastrointestinal tract diseases classification. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024; 34. [DOI: 10.1002/ima.23081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/31/2024] [Indexed: 09/23/2024]
Abstract
AbstractThe classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐related deaths. The complexities involved in resolving gastrointestinal tract (GIT) ailments arise from the need for elaborate methods to precisely identify the exact location of the problem. Therefore, doctors frequently use wireless capsule endoscopy to diagnose and treat GIT problems. This research aims to develop a robust framework using deep learning techniques to effectively classify GIT diseases for therapeutic purposes. A CNN based framework, in conjunction with the feature selection method, has been proposed to improve the classification rate. The proposed framework has been evaluated using various performance measures, including accuracy, recall, precision, F1 measure, mean absolute error, and mean squared error.
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Affiliation(s)
- Samra Siddiqui
- Department of Computer Science HITEC University Taxila Pakistan
- Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan
| | - Tallha Akram
- Department of Information Systems, College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al‐Kharj Saudi Arabia
- Department of Machine Learning Convex Solutions Pvt (Ltd) Islamabad Pakistan
| | - Imran Ashraf
- Department of Computer Science, Department of Computer Science NUCES (FAST) Islamabad Pakistan
| | - Muddassar Raza
- Department of Computer Science HITEC University Taxila Pakistan
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7
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Jiang B, Dorosan M, Leong JWH, Ong MEH, Lam SSW, Ang TL. Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution. Singapore Med J 2024; 65:133-140. [PMID: 38527297 PMCID: PMC11060635 DOI: 10.4103/singaporemedj.smj-2023-187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/10/2023] [Indexed: 03/27/2024]
Abstract
INTRODUCTION Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. METHODS We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.
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Affiliation(s)
- Bochao Jiang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
| | - Michael Dorosan
- Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore
| | - Justin Wen Hao Leong
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore
| | - Sean Shao Wei Lam
- Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
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Pan X, Mu Y, Ma C, He Q. TFCNet: A texture-aware and fine-grained feature compensated polyp detection network. Comput Biol Med 2024; 171:108144. [PMID: 38382386 DOI: 10.1016/j.compbiomed.2024.108144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/14/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE Abnormal tissue detection is a prerequisite for medical image analysis and computer-aided diagnosis and treatment. The use of neural networks (CNN) to achieve accurate detection of intestinal polyps is beneficial to the early diagnosis and treatment of colorectal cancer. Currently, image detection models using multi-scale feature processing perform well in polyp detection. However, these methods do not fully consider the misalignment of information in the process of feature scale change, resulting in the loss of fine-grained features, and eventually cause the missed and false detection of targets. METHOD To solve this problem, a texture-aware and fine-grained feature compensated polyp detection network (TFCNet) is proposed in this paper. Firstly, design Texture Awareness Module (TAM) to excavate the rich texture information from the low-level layers and utilize high-level semantic information for background suppression, thereby capturing purer fine-grained features. Secondly, the Texture Feature Enhancement Module (TFEM) is designed to enhance the low-level texture information in TAM, and the enhanced texture features were fused with the high-level features. By making full use of the low-level texture features and multi-scale context information, the semantic consistency and integrity of the features were ensured. Finally, the Residual Pyramid Splittable Attention Module (RPSA) is designed to balance the loss of channel information caused by skip connections, and further improve the detection performance of the network. RESULTS Experimental results on 4 datasets demonstrate that the TFCNet network outperforms existing methods. Particularly, on the large dataset PolypSets, the mAP@0.5-0.95 has been improved to 88.9%. On the small datasets CVC-ClinicDB and Kvasir, the mAP@0.5-0.95 is increased by 2% and 1.6%, respectively, compared to the baseline, showcasing a significant superiority over competing methods.
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Affiliation(s)
- Xiaoying Pan
- Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China.
| | - Yaya Mu
- Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China
| | - Chenyang Ma
- Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China
| | - Qiqi He
- Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China
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Kiziloluk S, Yildirim M, Bingol H, Alatas B. Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases. PeerJ Comput Sci 2024; 10:e1919. [PMID: 38435605 PMCID: PMC10909187 DOI: 10.7717/peerj-cs.1919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.
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Affiliation(s)
- Soner Kiziloluk
- Computer Engineering, Malatya Turgut Ozal University, Malatya, Turkey
| | - Muhammed Yildirim
- Computer Engineering, Malatya Turgut Ozal University, Malatya, Turkey
| | - Harun Bingol
- Software Engineering, Malatya Turgut Ozal University, Malatya, Turkey
| | - Bilal Alatas
- Software Engineering, Firat (Euphrates) University, Elazig, Turkey
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10
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Guo X, Xu L, Liu Z, Hao Y, Wang P, Zhu H, Du Y. Automated classification of ulcerative lesions in small intestine using densenet with channel attention and residual dilated blocks. Phys Med Biol 2024; 69:055017. [PMID: 38316034 DOI: 10.1088/1361-6560/ad2637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Objective. Ulceration of the small intestine, which has a high incidence, includes Crohn's disease (CD), intestinal tuberculosis (ITB), primary small intestinal lymphoma (PSIL), cryptogenic multifocal ulcerous stenosing enteritis (CMUSE), and non-specific ulcer (NSU). However, the ulceration morphology can easily be misdiagnosed through enteroscopy.Approach. In this study, DRCA-DenseNet169, which is based on DenseNet169, with residual dilated blocks and a channel attention block, is proposed to identify CD, ITB, PSIL, CMUSE, and NSU intelligently. In addition, a novel loss function that incorporates dynamic weights is designed to enhance the precision of imbalanced datasets with limited samples. DRCA-Densenet169 was evaluated using 10883 enteroscopy images, including 5375 ulcer images and 5508 normal images, which were obtained from the Shanghai Changhai Hospital.Main results. DRCA-Densenet169 achieved an overall accuracy of 85.27% ± 0.32%, a weighted-precision of 83.99% ± 2.47%, a weighted-recall of 84.36% ± 0.88% and a weighted-F1-score of 84.07% ± 2.14%.Significance. The results demonstrate that DRCA-Densenet169 has high recognition accuracy and strong robustness in identifying different types of ulcers when obtaining immediate and preliminary diagnoses.
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Affiliation(s)
- Xudong Guo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Lei Xu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Zhang Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Youguo Hao
- Department of Rehabilitation, Shanghai Putuo People's Hospital, Shanghai 200060, People's Republic of China
| | - Peng Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Huiyun Zhu
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, People's Republic of China
| | - Yiqi Du
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, People's Republic of China
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Karunanayake N, Moodleah S, Makhanov SS. Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation. Diagnostics (Basel) 2023; 13:3611. [PMID: 38132195 PMCID: PMC10742763 DOI: 10.3390/diagnostics13243611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/05/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications.
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Affiliation(s)
- Nalan Karunanayake
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand;
| | - Samart Moodleah
- King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;
| | - Stanislav S. Makhanov
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand;
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12
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Ramzan M, Raza M, Sharif MI, Azam F, Kim J, Kadry S. Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. PLoS One 2023; 18:e0292601. [PMID: 37831692 PMCID: PMC10575542 DOI: 10.1371/journal.pone.0292601] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.
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Affiliation(s)
- Muhammad Ramzan
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
| | - Muhammad Irfan Sharif
- Department of Information Sciences, University of Education Lahore, Jauharabad Campus, Jauharabad, Pakistan
| | - Faisal Azam
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Jungeun Kim
- Department of Software and CMPSI, Kongju National University, Cheonan, Korea
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- MEU Research Unit, Middle East University, Amman, Jordan
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13
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Poirier AC, Riaño Moreno RD, Takaindisa L, Carpenter J, Mehat JW, Haddon A, Rohaim MA, Williams C, Burkhart P, Conlon C, Wilson M, McClumpha M, Stedman A, Cordoni G, Branavan M, Tharmakulasingam M, Chaudhry NS, Locker N, Fernando A, Balachandran W, Bullen M, Collins N, Rimer D, Horton DL, Munir M, La Ragione RM. VIDIIA Hunter diagnostic platform: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostics approved for medical use in the UK. Front Mol Biosci 2023; 10:1144001. [PMID: 37842636 PMCID: PMC10572354 DOI: 10.3389/fmolb.2023.1144001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction: Accurate and rapid diagnostics paired with effective tracking and tracing systems are key to halting the spread of infectious diseases, limiting the emergence of new variants and to monitor vaccine efficacy. The current gold standard test (RT-qPCR) for COVID-19 is highly accurate and sensitive, but is time-consuming, and requires expensive specialised, lab-based equipment. Methods: Herein, we report on the development of a SARS-CoV-2 (COVID-19) rapid and inexpensive diagnostic platform that relies on a reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay and a portable smart diagnostic device. Automated image acquisition and an Artificial Intelligence (AI) deep learning model embedded in the Virus Hunter 6 (VH6) device allow to remove any subjectivity in the interpretation of results. The VH6 device is also linked to a smartphone companion application that registers patients for swab collection and manages the entire process, thus ensuring tests are traced and data securely stored. Results: Our designed AI-implemented diagnostic platform recognises the nucleocapsid protein gene of SARS-CoV-2 with high analytical sensitivity and specificity. A total of 752 NHS patient samples, 367 confirmed positives for coronavirus disease (COVID-19) and 385 negatives, were used for the development and validation of the test and the AI-assisted platform. The smart diagnostic platform was then used to test 150 positive clinical samples covering a dynamic range of clinically meaningful viral loads and 250 negative samples. When compared to RT-qPCR, our AI-assisted diagnostics platform was shown to be reliable, highly specific (100%) and sensitive (98-100% depending on viral load) with a limit of detection of 1.4 copies of RNA per µL in 30 min. Using this data, our CE-IVD and MHRA approved test and associated diagnostic platform has been approved for medical use in the United Kingdom under the UK Health Security Agency's Medical Devices (Coronavirus Test Device Approvals, CTDA) Regulations 2022. Laboratory and in-silico data presented here also indicates that the VIDIIA diagnostic platform is able to detect the main variants of concern in the United Kingdom (September 2023). Discussion: This system could provide an efficient, time and cost-effective platform to diagnose SARS-CoV-2 and other infectious diseases in resource-limited settings.
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Affiliation(s)
- Aurore C. Poirier
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | | | - Leona Takaindisa
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Jessie Carpenter
- VIDIIA Ltd., Surrey Technology Centre, Guildford, United Kingdom
| | - Jai W. Mehat
- Department of Microbial Sciences, School of Biosciences, University of Surrey, Guildford, United Kingdom
| | - Abi Haddon
- Berkshire and Surrey Pathology Services, Molecular Diagnostics, Royal Surrey County Hospital, Guildford, United Kingdom
| | - Mohammed A. Rohaim
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster, United Kingdom
| | - Craig Williams
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS Foundation Trust, Kendal, United Kingdom
| | - Peter Burkhart
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS Foundation Trust, Kendal, United Kingdom
| | - Chris Conlon
- GB Electronics (UK) Ltd, Worthing, United Kingdom
| | | | | | - Anna Stedman
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Guido Cordoni
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Manoharanehru Branavan
- College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom
| | | | - Nouman S. Chaudhry
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom
| | - Nicolas Locker
- Department of Microbial Sciences, School of Biosciences, University of Surrey, Guildford, United Kingdom
| | - Anil Fernando
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom
| | - Wamadeva Balachandran
- College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Mark Bullen
- GB Electronics (UK) Ltd, Worthing, United Kingdom
| | - Nadine Collins
- Berkshire and Surrey Pathology Services, Molecular Diagnostics, Royal Surrey County Hospital, Guildford, United Kingdom
| | - David Rimer
- VIDIIA Ltd., Surrey Technology Centre, Guildford, United Kingdom
| | - Daniel L. Horton
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Muhammad Munir
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster, United Kingdom
| | - Roberto M. La Ragione
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
- Department of Microbial Sciences, School of Biosciences, University of Surrey, Guildford, United Kingdom
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14
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Sharma A, Kumar R, Garg P. Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. Int J Med Inform 2023; 177:105142. [PMID: 37422969 DOI: 10.1016/j.ijmedinf.2023.105142] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. METHODS Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. RESULTS The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. CONCLUSION The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab 160062, India
| | - Rajnish Kumar
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab 160062, India.
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15
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Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
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Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
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16
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Naz J, Sharif MI, Sharif MI, Kadry S, Rauf HT, Ragab AE. A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features. Biomedicines 2023; 11:1723. [PMID: 37371819 DOI: 10.3390/biomedicines11061723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.
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Affiliation(s)
- Javeria Naz
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
| | - Muhammad Irfan Sharif
- Department of Computer Science, University of Education Lahore, Jauharabad Campus, Lahore 54770, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
- MEU Research Unit, Middle East University, Amman 11831, Jordan
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Adham E Ragab
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
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17
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Wu Y, Yan J, Xu Z, Sui G, Qi M, Geng Y, Wang J. Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050809. [PMID: 37238564 DOI: 10.3390/e25050809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023]
Abstract
Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.
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Affiliation(s)
- Yanze Wu
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jing Yan
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhuofan Xu
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Guoqing Sui
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Meirong Qi
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yingsan Geng
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jianhua Wang
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
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18
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Ghaleb Al-Mekhlafi Z, Mohammed Senan E, Sulaiman Alshudukhi J, Abdulkarem Mohammed B. Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8616939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.
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Affiliation(s)
- Zeyad Ghaleb Al-Mekhlafi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, Yemen
| | - Jalawi Sulaiman Alshudukhi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
| | - Badiea Abdulkarem Mohammed
- Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
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19
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Umer MJ, Sharif MI. A Comprehensive Survey on Quantum Machine Learning and Possible Applications. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2022. [DOI: 10.4018/ijehmc.315730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
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20
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Khan MA, Sharif MI, Raza M, Anjum A, Saba T, Shad SA. Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection. EXPERT SYSTEMS 2022; 39. [DOI: 10.1111/exsy.12497] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 10/25/2019] [Indexed: 08/25/2024]
Abstract
AbstractAutomated skin lesion diagnosis from dermoscopic images is a difficult process due to several notable problems such as artefacts (hairs), irregularity, lesion shape, and irrelevant features extraction. These problems make the segmentation and classification process difficult. In this research, we proposed an optimized colour feature (OCF) of lesion segmentation and deep convolutional neural network (DCNN)‐based skin lesion classification. A hybrid technique is proposed to remove the artefacts and improve the lesion contrast. Then, colour segmentation technique is presented known as OCFs. The OCF approach is further improved by an existing saliency approach, which is fused by a novel pixel‐based method. A DCNN‐9 model is implemented to extract deep features and fused with OCFs by a novel parallel fusion approach. After this, a normal distribution‐based high‐ranking feature selection technique is utilized to select the most robust features for classification. The suggested method is evaluated on ISBI series (2016, 2017, and 2018) datasets. The experiments are performed in two steps and achieved average segmentation accuracy of more than 90% on selected datasets. Moreover, the achieve classification accuracy of 92.1%, 96.5%, and 85.1%, respectively, on all three datasets shows that the presented method has remarkable performance.
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Affiliation(s)
| | - Muhammad Imran Sharif
- Department of Computer Science COMSATS University Islamabad, Wah Campus Islamabad Pakistan
| | - Mudassar Raza
- Department of Computer Science COMSATS University Islamabad, Wah Campus Islamabad Pakistan
| | - Almas Anjum
- Department of Computer Science University of Wah Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia
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21
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Son G, Eo T, An J, Oh DJ, Shin Y, Rha H, Kim YJ, Lim YJ, Hwang D. Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics (Basel) 2022; 12:diagnostics12081858. [PMID: 36010210 PMCID: PMC9406835 DOI: 10.3390/diagnostics12081858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 12/22/2022] Open
Abstract
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
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Affiliation(s)
- Geonhui Son
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Jiwoong An
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Hyenogseop Rha
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - You Jin Kim
- IntroMedic, Capsule Endoscopy Medical Device Manufacturer, Seoul 08375, Korea;
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
- Correspondence: (Y.J.L.); (D.H.)
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Korea
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.J.L.); (D.H.)
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22
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Raut V, Gunjan R, Shete VV, Eknath UD. Gastrointestinal tract disease segmentation and classification in wireless capsule endoscopy using intelligent deep learning model. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2099298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Vrushali Raut
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Reena Gunjan
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Virendra V. Shete
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Upasani Dhananjay Eknath
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
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23
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Mohammad F, Al-Razgan M. Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:2801. [PMID: 35408415 PMCID: PMC9003289 DOI: 10.3390/s22072801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/26/2022] [Accepted: 04/02/2022] [Indexed: 01/10/2023]
Abstract
Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.
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Affiliation(s)
- Farah Mohammad
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Muna Al-Razgan
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
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24
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Liu F, Liu X, Yin C, Wang H. Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm. Gastroenterol Res Pract 2022; 2022:7874751. [PMID: 35035476 PMCID: PMC8758331 DOI: 10.1155/2022/7874751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/29/2021] [Accepted: 12/06/2021] [Indexed: 11/23/2022] Open
Abstract
Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good-excellent prognostic efficacy, and it outperformed other traditional models.
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Affiliation(s)
- Fang Liu
- Neurosurgery Department, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, China
| | - Xiaoli Liu
- Department of Infection Management, Dongying People's Hospital, China
| | - Changyou Yin
- Neurosurgery Department, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, China
| | - Hongrong Wang
- Emergency Department, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, China
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25
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Rangaraj S, Islam M, Vs V, Wijethilake N, Uppal U, See AAQ, Chan J, James ML, King NKK, Ren H. Identifying risk factors of intracerebral hemorrhage stability using explainable attention model. Med Biol Eng Comput 2021; 60:337-348. [PMID: 34859369 DOI: 10.1007/s11517-021-02459-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 09/29/2021] [Indexed: 10/19/2022]
Abstract
Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. Graphical abstract Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP).
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Affiliation(s)
- Seshasayi Rangaraj
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Mobarakol Islam
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore, Singapore
| | - Vibashan Vs
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Navodini Wijethilake
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ENTC, University of Moratuwa, Moratuwa, Sri Lanka
| | - Utkarsh Uppal
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of Electrical Engineering, Punjab Engineering College, Chandigarh, India
| | - Angela An Qi See
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Jasmine Chan
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | | | - Nicolas Kon Kam King
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore.,Neuro Asia Care, Mount Elizabeth Hospital, Singapore, Singapore
| | - Hongliang Ren
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore. .,Department of Electronic Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong.
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26
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Zhu M, Chen Z, Yuan Y. DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3315-3325. [PMID: 34033538 DOI: 10.1109/tmi.2021.3083586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Automatic classification and segmentation of wireless capsule endoscope (WCE) images are two clinically significant and relevant tasks in a computer-aided diagnosis system for gastrointestinal diseases. Most of existing approaches, however, considered these two tasks individually and ignored their complementary information, leading to limited performance. To overcome this bottleneck, we propose a deep synergistic interaction network (DSI-Net) for joint classification and segmentation with WCE images, which mainly consists of the classification branch (C-Branch), the coarse segmentation (CS-Branch) and the fine segmentation branches (FS-Branch). In order to facilitate the classification task with the segmentation knowledge, a lesion location mining (LLM) module is devised in C-Branch to accurately highlight lesion regions through mining neglected lesion areas and erasing misclassified background areas. To assist the segmentation task with the classification prior, we propose a category-guided feature generation (CFG) module in FS-Branch to improve pixel representation by leveraging the category prototypes of C-Branch to obtain the category-aware features. In such way, these modules enable the deep synergistic interaction between these two tasks. In addition, we introduce a task interaction loss to enhance the mutual supervision between the classification and segmentation tasks and guarantee the consistency of their predictions. Relying on the proposed deep synergistic interaction mechanism, DSI-Net achieves superior classification and segmentation performance on public dataset in comparison with state-of-the-art methods. The source code is available at https://github.com/CityU-AIM-Group/DSI-Net.
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27
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Garg V, Sahoo A, Saxena V. A cognitive approach to endometrial tuberculosis identification using hierarchical deep fusion method. Soft comput 2021. [DOI: 10.1007/s00500-021-06474-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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28
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Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization. MATHEMATICS 2021. [DOI: 10.3390/math9192499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model.
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29
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Guo X, Zhang L, Hao Y, Zhang L, Liu Z, Liu J. Multiple abnormality classification in wireless capsule endoscopy images based on EfficientNet using attention mechanism. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:094102. [PMID: 34598534 DOI: 10.1063/5.0054161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
The wireless capsule endoscopy (WCE) procedure produces tens of thousands of images of the digestive tract, for which the use of the manual reading process is full of challenges. Convolutional neural networks are used to automatically detect lesions in WCE images. However, studies on clinical multilesion detection are scarce, and it is difficult to effectively balance the sensitivity to multiple lesions. A strategy for detecting multiple lesions is proposed, wherein common vascular and inflammatory lesions can be automatically and quickly detected on capsule endoscopic images. Based on weakly supervised learning, EfficientNet is fine-tuned to extract the endoscopic image features. Combining spatial features and channel features, the proposed attention network is then used as a classifier to obtain three classifications. The accuracy and speed of the model were compared with those of the ResNet121 and InceptionNetV4 models. It was tested on a public WCE image dataset obtained from 4143 subjects. On the computer-assisted diagnosis for capsule endoscopy database, the method gives a sensitivity of 96.67% for vascular lesions and 93.33% for inflammatory lesions. The precision for vascular lesions was 92.80%, and that for inflammatory lesions was 95.73%. The accuracy was 96.11%, which is 1.11% higher than that of the latest InceptionNetV4 network. Prediction for an image only requires 14 ms, which balances the accuracy and speed comparatively better. This strategy can be used as an auxiliary diagnostic method for specialists for the rapid reading of clinical capsule endoscopes.
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Affiliation(s)
- Xudong Guo
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Lulu Zhang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Youguo Hao
- Department of Rehabilitation, Shanghai Putuo People's Hospital, Shanghai 200060, China
| | - Linqi Zhang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zhang Liu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Jiannan Liu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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30
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N. Diniz D, T. Rezende M, G. C. Bianchi A, M. Carneiro C, J. S. Luz E, J. P. Moreira G, M. Ushizima D, N. S. de Medeiros F, J. F. Souza M. A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification. J Imaging 2021; 7:111. [PMID: 39080899 PMCID: PMC8321382 DOI: 10.3390/jimaging7070111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/17/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022] Open
Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals' workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
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Affiliation(s)
- Débora N. Diniz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Mariana T. Rezende
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Andrea G. C. Bianchi
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Claudia M. Carneiro
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Eduardo J. S. Luz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Gladston J. P. Moreira
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Daniela M. Ushizima
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA
| | - Fátima N. S. de Medeiros
- Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza 60455-970, Brazil;
| | - Marcone J. F. Souza
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
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31
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Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10481-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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32
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Naz J, Sharif M, Yasmin M, Raza M, Khan MA. Detection and Classification of Gastrointestinal Diseases using Machine Learning. Curr Med Imaging 2021; 17:479-490. [PMID: 32988355 DOI: 10.2174/1573405616666200928144626] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it's a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. METHODS In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. RESULTS A comparative analysis of different approaches for the detection and classification of GI infections. CONCLUSION This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.
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Affiliation(s)
- Javeria Naz
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
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33
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3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00328-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractWireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.
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34
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Attallah O, Sharkas M. GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. PeerJ Comput Sci 2021; 7:e423. [PMID: 33817058 PMCID: PMC7959662 DOI: 10.7717/peerj-cs.423] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/11/2021] [Indexed: 05/04/2023]
Abstract
Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communication Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
| | - Maha Sharkas
- Department of Electronics and Communication Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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35
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Liaqat A, Khan MA, Sharif M, Mittal M, Saba T, Manic KS, Al Attar FNH. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Curr Med Imaging 2021; 16:1229-1242. [PMID: 32334504 DOI: 10.2174/1573405616666200425220513] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/14/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022]
Abstract
Recent facts and figures published in various studies in the US show that approximately
27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that
the mortality rate is quite high in diagnosed cases. The early detection of these infections can save
precious human lives. As the manual process of these infections is time-consuming and expensive,
therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy
specialists in their clinics. Generally, an automated method of gastric infection detections
using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing,
feature extraction, segmentation of infected regions, and classification into their relevant
categories. These steps consist of various challenges that reduce the detection and recognition
accuracy as well as increase the computation time. In this review, authors have focused on the importance
of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and
the scope of endoscopy. Further, the general steps and highlighting the importance of each step
have been presented. A detailed discussion and future directions have been provided at the end.
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Affiliation(s)
- Amna Liaqat
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | | | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mamta Mittal
- Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, New Delhi, India
| | - Tanzila Saba
- Department of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - K. Suresh Manic
- Department of Electrical & Computer Engineering, National University of Science & Technology, Muscat, Oman
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36
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Zahoor S, Lali IU, Khan MA, Javed K, Mehmood W. Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review. Curr Med Imaging 2021; 16:1187-1200. [PMID: 32250226 DOI: 10.2174/1573405616666200406110547] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/25/2019] [Accepted: 01/03/2020] [Indexed: 11/22/2022]
Abstract
Breast Cancer is a common dangerous disease for women. Around the world, many women have died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues, there are several techniques and methods. The image processing, machine learning, and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to save a women's life. To detect the breast masses, microcalcifications, and malignant cells,different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are also challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.
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Affiliation(s)
- Saliha Zahoor
- Department of Computer Science, University of Gujrat, Gujrat, Pakistan
| | - Ikram Ullah Lali
- Department of Information Technology, University of Education, Lahore, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Museum Road Taxila, Rawalpindi, Pakistan
| | - Kashif Javed
- Department of Robotics, SMME NUST, Islamabad, Pakistan
| | - Waqar Mehmood
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
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37
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Rahim T, Usman MA, Shin SY. A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging. Comput Med Imaging Graph 2020; 85:101767. [DOI: 10.1016/j.compmedimag.2020.101767] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 07/13/2020] [Accepted: 07/18/2020] [Indexed: 12/12/2022]
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38
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Rohaim MA, Clayton E, Sahin I, Vilela J, Khalifa ME, Al-Natour MQ, Bayoumi M, Poirier AC, Branavan M, Tharmakulasingam M, Chaudhry NS, Sodi R, Brown A, Burkhart P, Hacking W, Botham J, Boyce J, Wilkinson H, Williams C, Whittingham-Dowd J, Shaw E, Hodges M, Butler L, Bates MD, La Ragione R, Balachandran W, Fernando A, Munir M. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. Viruses 2020; 12:v12090972. [PMID: 32883050 PMCID: PMC7552048 DOI: 10.3390/v12090972] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/22/2020] [Accepted: 08/24/2020] [Indexed: 01/13/2023] Open
Abstract
Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
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Affiliation(s)
- Mohammed A. Rohaim
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Emily Clayton
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Irem Sahin
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Julianne Vilela
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Manar E. Khalifa
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Mohammad Q. Al-Natour
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Mahmoud Bayoumi
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Aurore C. Poirier
- Department of Pathology and Infectious Diseases, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.C.P.); (R.L.R.)
| | - Manoharanehru Branavan
- College of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK; (M.B.); (W.B.)
| | - Mukunthan Tharmakulasingam
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK; (M.T.); (N.S.C.); (A.F.)
| | - Nouman S. Chaudhry
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK; (M.T.); (N.S.C.); (A.F.)
| | - Ravinder Sodi
- Department of Biochemistry, Poole & Bournemouth Hospitals NHS Trust, Longfleet Road, Poole BH15 2JB, UK;
| | - Amy Brown
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Peter Burkhart
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Wendy Hacking
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Judy Botham
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Joe Boyce
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Hayley Wilkinson
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Craig Williams
- The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK; (A.B.); (P.B.); (W.H.); (J.B.); (J.B.); (H.W.); (C.W.)
| | - Jayde Whittingham-Dowd
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Elisabeth Shaw
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Matt Hodges
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Lisa Butler
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Michelle D. Bates
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
| | - Roberto La Ragione
- Department of Pathology and Infectious Diseases, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.C.P.); (R.L.R.)
| | - Wamadeva Balachandran
- College of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK; (M.B.); (W.B.)
| | - Anil Fernando
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK; (M.T.); (N.S.C.); (A.F.)
| | - Muhammad Munir
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK; (M.A.R.); (E.C.); (I.S.); (J.V.); (M.E.K.); (M.Q.A.-N.); (M.B.); (J.W.-D.); (E.S.); (M.H.); (L.B.); (M.D.B.)
- Correspondence:
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Hwang M, Wang D, Kong XX, Wang Z, Li J, Jiang WC, Hwang KS, Ding K. An automated detection system for colonoscopy images using a dual encoder-decoder model. Comput Med Imaging Graph 2020; 84:101763. [PMID: 32805673 DOI: 10.1016/j.compmedimag.2020.101763] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 07/10/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
Abstract
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.
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Affiliation(s)
- Maxwell Hwang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Da Wang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiang-Xing Kong
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhanhuai Wang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun Li
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei-Cheng Jiang
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China
| | - Kao-Shing Hwang
- Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, China
| | - Kefeng Ding
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Rashid M, Khan MA, Alhaisoni M, Wang SH, Naqvi SR, Rehman A, Saba T. A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection. SUSTAINABILITY 2020; 12:5037. [DOI: 10.3390/su12125037] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.
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Affiliation(s)
- Muhammad Rashid
- Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
| | | | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
| | - Shui-Hua Wang
- School of Architecture Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
| | - Syed Rameez Naqvi
- Department of EE, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
| | - Amjad Rehman
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Tanzila Saba
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
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41
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J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05015-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Khan MA, Khan MA, Ahmed F, Mittal M, Goyal LM, Jude Hemanth D, Satapathy SC. Gastrointestinal diseases segmentation and classification based on duo-deep architectures. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.12.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Majid A, Khan MA, Yasmin M, Rehman A, Yousafzai A, Tariq U. Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection. Microsc Res Tech 2020; 83:562-576. [PMID: 31984630 DOI: 10.1002/jemt.23447] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 12/28/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%.
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Affiliation(s)
- Abdul Majid
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University Museum Road, Taxila, Rawalpindi, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Amjad Rehman
- AIDA Lab CCIS, Prince Sultan University Riyadh, Riyadh, Saudi Arabia
| | - Abdullah Yousafzai
- Department of Computer Science, HITEC University Museum Road, Taxila, Rawalpindi, Pakistan
| | - Usman Tariq
- College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RS. Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett 2020; 129:293-303. [DOI: 10.1016/j.patrec.2019.11.034] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Khan MA, Sarfraz MS, Alhaisoni M, Albesher AA, Wang S, Ashraf I. StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification. IEEE ACCESS 2020; 8:197969-197981. [DOI: 10.1109/access.2020.3034217] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Affiliation(s)
| | - Muhammad Shahzad Sarfraz
- Department of Computer Science, National University of Computer and Emerging Sciences at Chiniot–Faisalabad Campus, Chiniot, Pakistan
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia
| | - Abdulaziz A. Albesher
- College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Shuihua Wang
- Department of Mathematics, University of Leicester, Leicester, U.K
| | - Imran Ashraf
- Department of Computer Science, HITEC University, Taxila, Pakistan
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Khan MA, Kadry S, Alhaisoni M, Nam Y, Zhang Y, Rajinikanth V, Sarfraz MS. Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection. IEEE ACCESS 2020; 8:132850-132859. [DOI: 10.1109/access.2020.3010448] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
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Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.019] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Arshad H, Khan MA, Sharif M, Yasmin M, Javed MY. Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. INT J MACH LEARN CYB 2019; 10:3601-3618. [DOI: 10.1007/s13042-019-00947-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 03/11/2019] [Indexed: 12/18/2022]
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Khan MA, Sharif M, Akram T, Yasmin M, Nayak RS. Stomach Deformities Recognition Using Rank-Based Deep Features Selection. J Med Syst 2019; 43:329. [PMID: 31676931 DOI: 10.1007/s10916-019-1466-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 09/26/2019] [Indexed: 12/22/2022]
Abstract
Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
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Affiliation(s)
| | - Muhammad Sharif
- Department of E&CE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan.
| | - Tallha Akram
- Information Science, Canara Engineering College, Mangaluru, Karnataka, India
| | - Mussarat Yasmin
- Department of E&CE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
| | - Ramesh Sunder Nayak
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
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