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Zhao X, Lai L, Li Y, Zhou X, Cheng X, Chen Y, Huang H, Guo J, Wang G. A lightweight bladder tumor segmentation method based on attention mechanism. Med Biol Eng Comput 2024; 62:1519-1534. [PMID: 38308022 DOI: 10.1007/s11517-024-03018-x] [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: 07/29/2023] [Accepted: 01/05/2024] [Indexed: 02/04/2024]
Abstract
In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.
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Affiliation(s)
- Xiushun Zhao
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, China
| | - Libing Lai
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Yunjiao Li
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, China
| | - Xiaochen Zhou
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Xiaofeng Cheng
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Yujun Chen
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Haohui Huang
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, China
| | - Jing Guo
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Gongxian Wang
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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Yadav DP, Kumar D, Jalal AS, Kumar A, Singh KU, Shah MA. Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network. Sci Rep 2023; 13:16988. [PMID: 37813973 PMCID: PMC10562409 DOI: 10.1038/s41598-023-44210-7] [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: 05/23/2023] [Accepted: 10/05/2023] [Indexed: 10/11/2023] Open
Abstract
Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.
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Affiliation(s)
- D P Yadav
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Deepak Kumar
- Department of Computer Science, NIT Meghalaya, Shillong, 793003, India
| | - Anand Singh Jalal
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Ankit Kumar
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Kamred Udham Singh
- School of Computing, Graphic Era Hill University, Dehradun, 248002, India
| | - Mohd Asif Shah
- Kebri Dehar University, Kebri Dehar, Ethiopia.
- Woxsen University, Kamkole, Sadasivpet, Hyderabad, Telangana, 502345, India.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
- Research Fellow, INTI International University, Persiaran Perdana BBN Putra, Nilai, Negeri Sembilan, 71800, Malaysia.
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Kitaguchi Y, Hayakawa R, Kawashima R, Matsushita K, Tanaka H, Kawasaki R, Fujino T, Usui S, Shimojyo H, Okazaki T, Nishida K. Deep-learning approach to detect childhood glaucoma based on periocular photograph. Sci Rep 2023; 13:10141. [PMID: 37349526 PMCID: PMC10287677 DOI: 10.1038/s41598-023-37389-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 06/21/2023] [Indexed: 06/24/2023] Open
Abstract
Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on periocular photographs. Primary gaze photographs of children diagnosed with glaucoma with appearance features (corneal opacity, corneal enlargement, and/or globe enlargement) were retrospectively collected from the database of a single referral center. DL framework with the RepVGG architecture was used to automatically recognize childhood glaucoma from photographs. The average receiver operating characteristic curve (AUC) of fivefold cross-validation was 0.91. When the fivefold result was assembled, the DL model achieved an AUC of 0.95 with a sensitivity of 0.85 and specificity of 0.94. The DL model showed comparable accuracy to the pediatric ophthalmologists and glaucoma specialists in diagnosing childhood glaucoma (0.90 vs 0.81, p = 0.22, chi-square test), outperforming the average of human examiners in the detection rate of childhood glaucoma in cases without corneal opacity (72% vs. 34%, p = 0.038, chi-square test), with a bilateral corneal enlargement (100% vs. 67%, p = 0.03), and without skin lesions (87% vs. 64%, p = 0.02). Hence, this DL model is a promising tool for diagnosing missed childhood glaucoma cases.
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Affiliation(s)
- Yoshiyuki Kitaguchi
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Rina Hayakawa
- Division of Health Science, Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Rumi Kawashima
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kenji Matsushita
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hisashi Tanaka
- Division of Health Science, Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Ryo Kawasaki
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Suita, Osaka, Japan
| | - Takahiro Fujino
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shinichi Usui
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimojyo
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Tomoyuki Okazaki
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kohji Nishida
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan
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Goceri E. Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev 2023; 56:1-45. [PMID: 37362888 PMCID: PMC10027281 DOI: 10.1007/s10462-023-10453-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.
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Affiliation(s)
- Evgin Goceri
- Department of Biomedical Engineering, Engineering Faculty, Akdeniz University, Antalya, Turkey
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Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning. INFORMATION 2023. [DOI: 10.3390/info14010030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.
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Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL. SENSORS 2022; 22:s22155823. [PMID: 35957380 PMCID: PMC9371081 DOI: 10.3390/s22155823] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 02/05/2023]
Abstract
An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.
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