1
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Liu Z, Shen L. CECT: Controllable ensemble CNN and transformer for COVID-19 image classification. Comput Biol Med 2024; 173:108388. [PMID: 38569235 DOI: 10.1016/j.compbiomed.2024.108388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/15/2024] [Accepted: 03/25/2024] [Indexed: 04/05/2024]
Abstract
The COVID-19 pandemic has resulted in hundreds of million cases and numerous deaths worldwide. Here, we develop a novel classification network CECT by controllable ensemble convolutional neural network and transformer to provide a timely and accurate COVID-19 diagnosis. The CECT is composed of a parallel convolutional encoder block, an aggregate transposed-convolutional decoder block, and a windowed attention classification block. Each block captures features at different scales from 28 × 28 to 224 × 224 from the input, composing enriched and comprehensive information. Different from existing methods, our CECT can capture features at both multi-local and global scales without any sophisticated module design. Moreover, the contribution of local features at different scales can be controlled with the proposed ensemble coefficients. We evaluate CECT on two public COVID-19 datasets and it reaches the highest accuracy of 98.1% in the intra-dataset evaluation, outperforming existing state-of-the-art methods. Moreover, the developed CECT achieves an accuracy of 90.9% on the unseen dataset in the inter-dataset evaluation, showing extraordinary generalization ability. With remarkable feature capture ability and generalization ability, we believe CECT can be extended to other medical scenarios as a powerful diagnosis tool. Code is available at https://github.com/NUS-Tim/CECT.
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Affiliation(s)
- Zhaoshan Liu
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
| | - Lei Shen
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
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2
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Berenguer AD, Kvasnytsia M, Bossa MN, Mukherjee T, Deligiannis N, Sahli H. Semi-supervised medical image classification via distance correlation minimization and graph attention regularization. Med Image Anal 2024; 94:103107. [PMID: 38401269 DOI: 10.1016/j.media.2024.103107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/11/2023] [Accepted: 02/13/2024] [Indexed: 02/26/2024]
Abstract
We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled deep neural networks architectures. In addition, it incorporates a data-driven graph-attention based regularization strategy to model affinities among images within the unlabeled data by exploiting their inherent relational information in the feature space. We conduct extensive experiments on four medical imaging benchmark data sets involving X-ray, dermoscopic, magnetic resonance, and computer tomography imaging on single and multi-label medical imaging classification scenarios. Our experiments demonstrate the effectiveness of our method in achieving very competitive performance and outperforming several state-of-the-art semi-supervised learning methods. Furthermore, they confirm the suitability of distance correlation as a versatile dependence measure and the benefits of the proposed graph-attention based regularization for semi-supervised learning in medical imaging analysis.
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Affiliation(s)
- Abel Díaz Berenguer
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium.
| | - Maryna Kvasnytsia
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
| | - Matías Nicolás Bossa
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
| | - Tanmoy Mukherjee
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
| | - Nikos Deligiannis
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium; Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Heverlee, Belgium
| | - Hichem Sahli
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium; Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Heverlee, Belgium
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3
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Asif S, Zhao M, Li Y, Tang F, Zhu Y. CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection. Neural Netw 2024; 173:106183. [PMID: 38382397 DOI: 10.1016/j.neunet.2024.106183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/19/2023] [Accepted: 02/15/2024] [Indexed: 02/23/2024]
Abstract
The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.
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Affiliation(s)
- Sohaib Asif
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Ming Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yangfan Li
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Fengxiao Tang
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yusen Zhu
- School of Mathematics, Hunan University, Changsha, China
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4
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Ebrahim M, Alsmirat M, Al-Ayyoub M. Advanced disk herniation computer aided diagnosis system. Sci Rep 2024; 14:8071. [PMID: 38580700 PMCID: PMC10997754 DOI: 10.1038/s41598-024-58283-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 03/27/2024] [Indexed: 04/07/2024] Open
Abstract
Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to boost the performance of such algorithms even further, which include various transfer learning techniques, data augmentation, and feature concatenation. Normally, the use of these advanced techniques highly depends on the size and nature of the dataset being used. In the case of fine-grained medical image sets, which have subcategories within the main categories in the image set, there is a need to find the combination of the techniques that work the best on these types of images. In this work, we utilize these advanced techniques to find the best combinations to build a state-of-the-art lumber disc herniation computer-aided diagnosis system. We have evaluated the system extensively and the results show that the diagnosis system achieves an accuracy of 98% when it is compared with human diagnosis.
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Affiliation(s)
- Maad Ebrahim
- Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, QC, H3T1J4, Canada
- Department of Computer Science, Jordan University of Science and Technology, Ar-Ramtha, Jordan
| | - Mohammad Alsmirat
- Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates.
- Department of Computer Science, Jordan University of Science and Technology, Ar-Ramtha, Jordan.
| | - Mahmoud Al-Ayyoub
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
- Department of Computer Science, Jordan University of Science and Technology, Ar-Ramtha, Jordan.
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5
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Han Q, Qian X, Xu H, Wu K, Meng L, Qiu Z, Weng T, Zhou B, Gao X. DM-CNN: Dynamic Multi-scale Convolutional Neural Network with uncertainty quantification for medical image classification. Comput Biol Med 2024; 168:107758. [PMID: 38042102 DOI: 10.1016/j.compbiomed.2023.107758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/30/2023] [Accepted: 11/21/2023] [Indexed: 12/04/2023]
Abstract
Convolutional neural network (CNN) has promoted the development of diagnosis technology of medical images. However, the performance of CNN is limited by insufficient feature information and inaccurate attention weight. Previous works have improved the accuracy and speed of CNN but ignored the uncertainty of the prediction, that is to say, uncertainty of CNN has not received enough attention. Therefore, it is still a great challenge for extracting effective features and uncertainty quantification of medical deep learning models In order to solve the above problems, this paper proposes a novel convolutional neural network model named DM-CNN, which mainly contains the four proposed sub-modules : dynamic multi-scale feature fusion module (DMFF), hierarchical dynamic uncertainty quantifies attention (HDUQ-Attention) and multi-scale fusion pooling method (MF Pooling) and multi-objective loss (MO loss). DMFF select different convolution kernels according to the feature maps at different levels, extract different-scale feature information, and make the feature information of each layer have stronger representation ability for information fusion HDUQ-Attention includes a tuning block that adjust the attention weight according to the different information of each layer, and a Monte-Carlo (MC) dropout structure for quantifying uncertainty MF Pooling is a pooling method designed for multi-scale models, which can speed up the calculation and prevent overfitting while retaining the main important information Because the number of parameters in the backbone part of DM-CNN is different from other modules, MO loss is proposed, which has a fast optimization speed and good classification effect DM-CNN conducts experiments on publicly available datasets in four areas of medicine (Dermatology, Histopathology, Respirology, Ophthalmology), achieving state-of-the-art classification performance on all datasets. DM-CNN can not only maintain excellent performance, but also solve the problem of quantification of uncertainty, which is a very important task for the medical field. The code is available: https://github.com/QIANXIN22/DM-CNN.
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Affiliation(s)
- Qi Han
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Xin Qian
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China.
| | - Hongxiang Xu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Kepeng Wu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Lun Meng
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Zicheng Qiu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Tengfei Weng
- School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Baoping Zhou
- School of Information Engineering, Tarim University, Alar City, 843300, PR China
| | - Xianqiang Gao
- School of Information Engineering, Tarim University, Alar City, 843300, PR China
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6
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Li X, Wu Q, Wang M, Wu K. Uncertainty-aware network for fine-grained and imbalanced reflux esophagitis grading. Comput Biol Med 2024; 168:107751. [PMID: 38016373 DOI: 10.1016/j.compbiomed.2023.107751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/22/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Computer-aided diagnosis (CAD) assists endoscopists in analyzing endoscopic images, reducing misdiagnosis rates and enabling timely treatment. A few studies have focused on CAD for gastroesophageal reflux disease, but CAD studies on reflux esophagitis (RE) are still inadequate. This paper presents a CAD study on RE using a dataset collected from hospital, comprising over 3000 images. We propose an uncertainty-aware network with handcrafted features, utilizing representation and classifier decoupling with metric learning to address class imbalance and achieve fine-grained RE classification. To enhance interpretability, the network estimates uncertainty through test time augmentation. The experimental results demonstrate that the proposed network surpasses previous methods, achieving an accuracy of 90.2% and an F1 score of 90.1%.
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Affiliation(s)
- Xingcun Li
- School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Qinghua Wu
- School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Mi Wang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Kun Wu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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Liu L, Zhang Y, Sun L. Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification. Health Inf Sci Syst 2023; 11:44. [PMID: 37771395 PMCID: PMC10522544 DOI: 10.1007/s13755-023-00246-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023] Open
Abstract
Efficient and accurate medical image classification (MIC) methods face two major challenges: (1) high similarity between images of different disease classes; and (2) generating large medical image datasets for training deep neural networks is challenging due to privacy restrictions and the need for expert ground truth annotations. In this paper, we introduce a novel deep learning method called pre-training grayscale images with supervised learning for MIC (MediMatrix). Instead of pre-training on color ImageNet, our approach uses MediMatrix on grayscale ImageNet. To improve the performance of the network, we introduce ShuffleAttention (SA), a self-attention mechanism. By combining SA with the multiple residual structure (ResSA block) and replacing short-cut connections with dense residual connections between corresponding layers (densepath), our network can dynamically adjust channel attention weights and receive image inputs of different sizes, resulting in improved feature representation and better discrimination of similarities between different categories. MediMatrix effectively classifies X-ray images of rheumatoid arthritis (RA), enabling efficient screening without the need for expert analysis or invasive testing. Through extensive experiments, we demonstrate the superiority of MediMatrix over state-of-the-art methods and that color is not critical for rich natural image classification. Our results highlight the potential of computer-aided diagnosis combined with MediMatrix as a valuable screening tool for early detection and intervention in RA.
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Affiliation(s)
- Linchen Liu
- Department of Rheumatology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009 China
| | - Yiyang Zhang
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Le Sun
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
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8
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Zhang R, Wang L, Cheng S, Song S. MLP-based classification of COVID-19 and skin diseases. Expert Syst Appl 2023; 228:120389. [PMID: 37193247 PMCID: PMC10170962 DOI: 10.1016/j.eswa.2023.120389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/18/2023]
Abstract
Recent years have witnessed a growing interest in neural network-based medical image classification methods, which have demonstrated remarkable performance in this field. Typically, convolutional neural network (CNN) architectures have been commonly employed to extract local features. However, the transformer, a newly emerged architecture, has gained popularity due to its ability to explore the relevance of remote elements in an image through a self-attention mechanism. Despite this, it is crucial to establish not only local connectivity but also remote relationships between lesion features and capture the overall image structure to improve image classification accuracy. Therefore, to tackle the aforementioned issues, this paper proposes a network based on multilayer perceptrons (MLPs) that can learn the local features of medical images on the one hand and capture the overall feature information in both spatial and channel dimensions on the other hand, thus utilizing image features effectively. This paper has been extensively validated on COVID19-CT dataset and ISIC 2018 dataset, and the results show that the method in this paper is more competitive and has higher performance in medical image classification compared with existing methods. This shows that the use of MLP to capture image features and establish connections between lesions is expected to provide novel ideas for medical image classification tasks in the future.
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Affiliation(s)
- Ruize Zhang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China
| | - Liejun Wang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China
| | - Shuli Cheng
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China
| | - Shiji Song
- Department of Automation, Tsinghua University, Beijing, 100084, China
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9
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Cibi A, Rose RJ. Classification of stages in cervical cancer MRI by customized CNN and transfer learning. Cogn Neurodyn 2023; 17:1261-1269. [PMID: 37786661 PMCID: PMC10542080 DOI: 10.1007/s11571-021-09777-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/08/2021] [Accepted: 12/23/2021] [Indexed: 11/30/2022] Open
Abstract
Cervical cancer is the common cancer among women, where early-stage diagnoses of cervical cancer lead to recovery from the deadly cervical cancer. Correct cervical cancer staging is predominant to decide the treatment. Hence, cervical cancer staging is an important problem in designing automatic detection and diagnosing applications of the medical field. Convolutional Neural Networks (CNNs) often plays a greater role in object identification and classification. The performance of CNN in medical image classification can already compete with radiologists. In this paper, we planned to build a deep Capsule Network (CapsNet) for medical image classification that can achieve high accuracy using cervical cancer Magnetic Resonance (MR) images. In this study, a customized deep CNN model is developed using CapsNet to automatically predict the cervical cancer from MR images. In CapsNet, each layer receives input from all preceding layers, which helps to classify the features. The hyper parameters are estimated and it controls the backpropagation gradient at the initial learning. To improve the CapsNet performance, residual blocks are included between dense layers. Training and testing are performed with around 12,771 T2-weighted MR images of the TCGA-CESC dataset publicly available for research work. The results show that the accuracy of Customized CNN using CapsNetis higher and behaves well in classifying the cervical cancer. Thus, it is evident that CNN models can be used in developing automatic image analysis tools in the medical field.
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Affiliation(s)
- A. Cibi
- Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India
| | - R. Jemila Rose
- Department of Computer Science and Engineering, St.Xavier’s Catholic College of Engineering, Nagercoil, India
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10
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Abd Elaziz M, Dahou A, Mabrouk A, El-Sappagh S, Aseeri AO. An Efficient Artificial Rabbits Optimization Based on Mutation Strategy For Skin Cancer Prediction. Comput Biol Med 2023; 163:107154. [PMID: 37364532 DOI: 10.1016/j.compbiomed.2023.107154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/26/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained Deep Learning (DL) models have been applied to tackle and improve efficiency on tasks such as skin cancer detection instead of training models from scratch. Therefore, we develop a robust model for skin cancer detection with a DL-based model as a feature extraction backbone, which is achieved using MobileNetV3 architecture. In addition, a novel algorithm called the Improved Artificial Rabbits Optimizer (IARO) is introduced, which uses the Gaussian mutation and crossover operator to ignore the unimportant features from those features extracted using MobileNetV3. The PH2, ISIC-2016, and HAM10000 datasets are used to validate the developed approach's efficiency. The empirical results show that the developed approach yields outstanding accuracy results of 87.17% on the ISIC-2016 dataset, 96.79% on the PH2 dataset, and 88.71 % on the HAM10000 dataset. Experiments show that the IARO can significantly improve the prediction of skin cancer.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon; MEU Research Unit, Middle East University, Amman 11831, Jordan.
| | - Abdelghani Dahou
- Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria.
| | - Alhassan Mabrouk
- Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt.
| | - Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt.
| | - Ahmad O Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
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11
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Huang Z, Wu J, Wang T, Li Z, Ioannou A. Class-Specific Distribution Alignment for semi-supervised medical image classification. Comput Biol Med 2023; 164:107280. [PMID: 37517324 DOI: 10.1016/j.compbiomed.2023.107280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/11/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes. Furthermore, we propose a Variable Condition Queue (VCQ) module to maintain a proportionately balanced number of unlabeled samples for each class. Experiments on three public datasets HAM10000, CheXpert and Kvasir show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks.
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Affiliation(s)
- Zhongzheng Huang
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Computer and Data Science, Fuzhou University, Fuzhou, China
| | - Jiawei Wu
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Tao Wang
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; International Digital Economy College, Minjiang University, Fuzhou, China.
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China.
| | - Anastasia Ioannou
- International Digital Economy College, Minjiang University, Fuzhou, China; Department of Computer Science and Engineering, European University Cyprus, Nicosia, Cyprus
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12
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Abbas Q, Malik KM, Saudagar AKJ, Khan MB. Context-aggregator: An approach of loss- and class imbalance-aware aggregation in federated learning. Comput Biol Med 2023; 163:107167. [PMID: 37421740 DOI: 10.1016/j.compbiomed.2023.107167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 07/10/2023]
Abstract
Federated Learning (FL) is an emerging distributed learning paradigm which offers data privacy to contributing nodes in the collaborating environment. By exploiting the Individual datasets of different hospitals in FL setting could be used to develop reliable screening, diagnosis, and treatment predictive models to tackle major challenges such as pandemics. FL can enable the development of very diverse medical imaging datasets and thus provide more reliable models for all participating nodes, including those with low quality data. However, the issue with the traditional Federated Learning paradigm is the degradation of generalization power due to poorly trained local models at the client nodes. The generalization power of the FL paradigm can be improved by considering the relative learning contribution of client nodes. Simple aggregation of learning parameters in the standard FL model faces a diversity issue and results in more validation loss during the learning process. This issue can be resolved by considering the relative contribution of each client node participating in the learning process. The class imbalance at each site is another significant challenge that greatly impacts the performance of the aggregated learning model. This work considers Context Aggregator FL based on the context of loss-factor and class-imbalance issues by incorporating the relative contribution of the collaborating nodes in FL by proposing Validation-Loss based Context Aggregator (CAVL) and Class Imbalance based Context Aggregator (CACI). The proposed Context Aggregator is evaluated on several different Covid-19 imaging classification datasets present on participating nodes. The evaluation results show that Context Aggregator performs better than standard Federating average Learning algorithms and FedProx Algorithm for Covid-19 image classification problems.
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Affiliation(s)
- Qamar Abbas
- Department of Computer Science, Faculty of Computing and Information Technology, International Islamic University, Islamabad, 44000, Pakistan
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309-4401, USA.
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia
| | - Muhammad Badruddin Khan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia
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13
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Zhu M, Zhang L, Wang L, Li D, Zhang J, Yi Z. Robust co-teaching learning with consistency-based noisy label correction for medical image classification. Int J Comput Assist Radiol Surg 2023; 18:675-683. [PMID: 36437387 DOI: 10.1007/s11548-022-02799-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/17/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Deep neural networks (DNNs) have made great achievements in computer-aided diagnostic systems, but the success highly depends on massive data with high-quality labels. However, for many medical image datasets, a considerable number of noisy labels are introduced by inter- and intra-observer variability, thus hampering DNNs' performance. To address this problem, a robust noisy label correction method with the co-teaching learning paradigm is proposed. METHODS The proposed method aims to reduce the effect of noisy labels by correcting or removing them. It consists of two modules. An adaptive noise rate estimation module is employed to calculate the dataset's noise rate, which is helpful to detect noisy labels but is usually unavailable in clinical applications. A consistency-based noisy label correction module aims to detect noisy labels and correct them to reduce the disturbance from noisy labels and exploit useful information in data. RESULTS Experiments are conducted on the public skin lesion dataset ISIC-2017, ISIC-2019, and our constructed thyroid ultrasound image dataset. The results demonstrate that the proposed method outperforms other noisy label learning methods in medical image classification tasks. It is also evaluated on the natural image dataset CIFAR-10 to show its generalization. CONCLUSION This paper proposes a noisy label correction method to handle noisy labels in medical image datasets. Experimental results show that it can self-adapt to different datasets and efficiently correct noisy labels, which is suitable for medical image classification.
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Affiliation(s)
- Minjuan Zhu
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China.
| | - Lituan Wang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Dong Li
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Jianwei Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Zhang Yi
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
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14
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Manzari ON, Ahmadabadi H, Kashiani H, Shokouhi SB, Ayatollahi A. MedViT: A robust vision transformer for generalized medical image classification. Comput Biol Med 2023; 157:106791. [PMID: 36958234 DOI: 10.1016/j.compbiomed.2023.106791] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 02/18/2023] [Accepted: 03/11/2023] [Indexed: 03/16/2023]
Abstract
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attacks since inaccurate diagnosis could lead to disastrous consequences in the safety realm. In this study, we propose a highly robust yet efficient CNN-Transformer hybrid model which is equipped with the locality of CNNs as well as the global connectivity of vision Transformers. To mitigate the high quadratic complexity of the self-attention mechanism while jointly attending to information in various representation subspaces, we construct our attention mechanism by means of an efficient convolution operation. Moreover, to alleviate the fragility of our Transformer model against adversarial attacks, we attempt to learn smoother decision boundaries. To this end, we augment the shape information of an image in the high-level feature space by permuting the feature mean and variance within mini-batches. With less computational complexity, our proposed hybrid model demonstrates its high robustness and generalization ability compared to the state-of-the-art studies on a large-scale collection of standardized MedMNIST-2D datasets.
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Affiliation(s)
- Omid Nejati Manzari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
| | - Hamid Ahmadabadi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hossein Kashiani
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, USA
| | - Shahriar B Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ahmad Ayatollahi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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15
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Yang X, Xi X, Yang L, Xu C, Song Z, Nie X, Qiao L, Li C, Shi Q, Yin Y. Multi-modality relation attention network for breast tumor classification. Comput Biol Med 2022; 150:106210. [PMID: 37859295 DOI: 10.1016/j.compbiomed.2022.106210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/05/2022] [Accepted: 10/09/2022] [Indexed: 11/03/2022]
Abstract
Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Within the proposed network, a novel multi-modality relation attention module improves the discriminative ability of single-modality features by exploring the correlation information between two modalities. In addition, a module ensures the classification consistency of ADC and DWI modality, thus improving robustness to noise. Experimental results on our database demonstrate that the proposed method is effective for breast tumor classification, and outperforms existing multi-modality fusion methods. The AUC, accuracy, specificity, and sensitivity are 85.1%, 86.7%, 83.3%, and 88.9% respectively.
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Affiliation(s)
- Xiao Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China.
| | - Lu Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Chuanzhen Xu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Zuoyong Song
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiushan Nie
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Lishan Qiao
- School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, China
| | - Chenglong Li
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Qinglei Shi
- Diagnosis Imaging, Siemens Healthcare Ltd, Beijing, 100102, China
| | - Yilong Yin
- School of Software, Shandong University, Jinan, 250101, China
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16
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T N SG, Satish R, Sridhar R. Learning effective embedding for automated COVID-19 prediction from chest X-ray images. Multimed Syst 2022; 29:739-751. [PMID: 36310764 PMCID: PMC9596346 DOI: 10.1007/s00530-022-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.
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Affiliation(s)
- Sree Ganesh T N
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
| | - Rishi Satish
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
| | - Rajeswari Sridhar
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
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17
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Zhao Y, Wang S, Ren Y, Zhang Y. CRANet: a comprehensive residual attention network for intracranial aneurysm image classification. BMC Bioinformatics 2022; 23:322. [PMID: 35931949 PMCID: PMC9356401 DOI: 10.1186/s12859-022-04872-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/02/2022] [Indexed: 11/15/2022] Open
Abstract
Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.
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Affiliation(s)
- Yawu Zhao
- College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China
| | - Shudong Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China.
| | - Yande Ren
- The Department of Medical Imaging Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yulin Zhang
- College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, Shandong, China.
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18
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Rodriguez D, Nayak T, Chen Y, Krishnan R, Huang Y. On the role of deep learning model complexity in adversarial robustness for medical images. BMC Med Inform Decis Mak 2022; 22:160. [PMID: 35725429 PMCID: PMC9208111 DOI: 10.1186/s12911-022-01891-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/23/2022] [Indexed: 11/29/2022] Open
Abstract
Background Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that it is benign. However, adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity—particularly, very large models. In this work, we investigate the role of model complexity in adversarial settings. Results Consider a set of DL models that exhibit similar performances for a given task. These models are trained in the usual manner but are not trained to defend against adversarial attacks. We demonstrate that, among those models, simpler models of reduced complexity show a greater level of robustness against adversarial attacks than larger models that often tend to be used in medical applications. On the other hand, we also show that once those models undergo adversarial training, the adversarial trained medical image DL models exhibit a greater degree of robustness than the standard trained models for all model complexities. Conclusion The above result has a significant practical relevance. When medical practitioners lack the expertise or resources to defend against adversarial attacks, we recommend that they select the smallest of the models that exhibit adequate performance. Such a model would be naturally more robust to adversarial attacks than the larger models.
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Affiliation(s)
- David Rodriguez
- Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA
| | - Tapsya Nayak
- Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Yidong Chen
- Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX, USA.,Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Ram Krishnan
- Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA
| | - Yufei Huang
- Department of Medicine, School of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, USA.
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19
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Aytaç UC, Güneş A, Ajlouni N. A novel adaptive momentum method for medical image classification using convolutional neural network. BMC Med Imaging 2022; 22:34. [PMID: 35232390 PMCID: PMC8886705 DOI: 10.1186/s12880-022-00755-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/07/2022] [Indexed: 12/25/2022] Open
Abstract
Background AI for medical diagnosis has made a tremendous impact by applying convolutional neural networks (CNNs) to medical image classification and momentum plays an essential role in stochastic gradient optimization algorithms for accelerating or improving training convolutional neural networks. In traditional optimizers in CNNs, the momentum is usually weighted by a constant. However, tuning hyperparameters for momentum can be computationally complex. In this paper, we propose a novel adaptive momentum for fast and stable convergence. Method Applying adaptive momentum rate proposes increasing or decreasing based on every epoch's error changes, and it eliminates the need for momentum hyperparameter optimization. We tested the proposed method with 3 different datasets: REMBRANDT Brain Cancer, NIH Chest X-ray, COVID-19 CT scan. We compared the performance of a novel adaptive momentum optimizer with Stochastic gradient descent (SGD) and other adaptive optimizers such as Adam and RMSprop. Results Proposed method improves SGD performance by reducing classification error from 6.12 to 5.44%, and it achieved the lowest error and highest accuracy compared with other optimizers. To strengthen the outcomes of this study, we investigated the performance comparison for the state-of-the-art CNN architectures with adaptive momentum. The results shows that the proposed method achieved the highest with 95% compared to state-of-the-art CNN architectures while using the same dataset. The proposed method improves convergence performance by reducing classification error and achieves high accuracy compared with other optimizers.
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Affiliation(s)
- Utku Can Aytaç
- Computer Engineering Department, Faculty of Computer Engineering, Istanbul Aydın University, Besyol, Inonu Cd. No: 38, 34295, Kucukcekmece, Istanbul, Turkey.
| | - Ali Güneş
- Computer Engineering Department, Istanbul Aydin University, Istanbul, Turkey
| | - Naim Ajlouni
- Computer Engineering Department, Istanbul Atlas University, Istanbul, Turkey
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20
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Lei Y, Zhang J, Shan H. Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification. Phenomics 2021; 1:257-268. [PMID: 36939784 PMCID: PMC9590543 DOI: 10.1007/s43657-021-00025-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 09/14/2021] [Indexed: 11/26/2022]
Abstract
Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.
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Affiliation(s)
- Yiming Lei
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433 China
| | - Junping Zhang
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433 China
| | - Hongming Shan
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, 201210 China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 201210 China
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21
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Biloborodova T, Skarga-Bandurova I, Koverha M, Skarha-Bandurov I, Yevsieieva Y. A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets. Stud Health Technol Inform 2021; 287:13-17. [PMID: 34795070 DOI: 10.3233/shti210801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industry. However, medical data characteristics such as relatively small datasets for rare diseases or imbalance in class distribution for rare conditions significantly restrains their adoption and reuse. Imbalanced datasets lead to difficulties in learning and obtaining accurate predictive models. This paper follows the FAIR paradigm and proposes a technique for the alignment of class distribution, which enables improving image classification performance in imbalanced data and ensuring data reuse. The experiments on the acne disease dataset support that the proposed framework outperforms the baselines and enable to achieve up to 5% improvement in image classification.
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Affiliation(s)
| | | | - Mark Koverha
- Volodymyr Dahl East Ukrainian National University, Ukraine
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22
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Abstract
Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer's disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages. Two methods are used to classify the medical images and detect AD. The first method uses simple CNN architectures that deal with 2D and 3D structural brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on 2D and 3D convolution. The second method applies the transfer learning principle to take advantage of the pre-trained models for medical image classifications, such as the VGG19 model. Due to the COVID-19 pandemic, it is difficult for people to go to hospitals periodically to avoid gatherings and infections. As a result, Alzheimer's checking web application is proposed using the final qualified proposed architectures. It helps doctors and patients to check AD remotely. It also determines the AD stage of the patient based on the AD spectrum and advises the patient according to its AD stage. Nine performance metrics are used in the evaluation and the comparison between the two methods. The experimental results prove that the CNN architectures for the first method have the following characteristics: suitable simple structures that reduce computational complexity, memory requirements, overfitting, and provide manageable time. Besides, they achieve very promising accuracies, 93.61% and 95.17% for 2D and 3D multi-class AD stage classifications. The VGG19 pre-trained model is fine-tuned and achieved an accuracy of 97% for multi-class AD stage classifications.
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Affiliation(s)
- Hadeer A. Helaly
- Electrical Engineering Department, Faculty of Engineering, Damietta University, Damietta, Egypt
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- Department of Computer Science and Informatics, Taibah University, Medina, Saudi Arabia
| | - Amira Y. Haikal
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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23
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Kudva V, Prasad K, Guruvare S. Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. J Digit Imaging 2020; 33:619-31. [PMID: 31848896 DOI: 10.1007/s10278-019-00269-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features are obtained. This is facilitated through the use of large number of filters present in the convolutional layers of pre-trained network. A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%.
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Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PUP. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int J Comput Assist Radiol Surg 2021; 16:1549-1563. [PMID: 34053009 DOI: 10.1007/s11548-021-02410-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 05/14/2021] [Indexed: 01/27/2023]
Abstract
PURPOSE Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate deep learning methods for automatic diagnosis, providing significant advantages over manual methods. In this paper, we propose a novel deep learning framework to perform multi-class cancer classification of liver hepatocellular carcinoma (HCC) tumor histopathology images which shows improvements in inference speed and classification quality over other competitive methods. METHOD The BreastNet architecture proposed by Togacar et al. shows great promise in using convolutional block attention modules (CBAM) for effective cancer classification in H&E stained breast histopathology images. As part of our experiments with this framework, we have studied the addition of atrous spatial pyramid pooling (ASPP) blocks to effectively capture multi-scale features in H&E stained liver histopathology data. We classify liver histopathology data into four classes, namely the non-cancerous class, low sub-type liver HCC tumor, medium sub-type liver HCC tumor, and high sub-type liver HCC tumor. To prove the robustness and efficacy of our models, we have shown results for two liver histopathology datasets-a novel KMC dataset and the TCGA dataset. RESULTS Our proposed architecture outperforms state-of-the-art architectures for multi-class cancer classification of HCC histopathology images, not just in terms of quality of classification, but also in computational efficiency on the novel proposed KMC liver data and the publicly available TCGA-LIHC dataset. We have considered precision, recall, F1-score, intersection over union (IoU), accuracy, number of parameters, and FLOPs as metrics for comparison. The results of our meticulous experiments have shown improved classification performance along with added efficiency. LiverNet has been observed to outperform all other frameworks in all metrics under comparison with an approximate improvement of [Formula: see text] in accuracy and F1-score on the KMC and TCGA-LIHC datasets. CONCLUSION To the best of our knowledge, our work is among the first to provide concrete proof and demonstrate results for a successful deep learning architecture to handle multi-class HCC histopathology image classification among various sub-types of liver HCC tumor. Our method shows a high accuracy of [Formula: see text] on the proposed KMC liver dataset requiring only 0.5739 million parameters and 1.1934 million floating point operations per second.
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Affiliation(s)
- Anirudh Ashok Aatresh
- Department of Electronics and Communication Engineering, National Institute Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India
| | - Kumar Alabhya
- Department of Electronics and Communication Engineering, National Institute Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India
| | - Shyam Lal
- Department of Electronics and Communication Engineering, National Institute Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India.
| | - Jyoti Kini
- Department of pathology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| | - P U Prakash Saxena
- Department of Radiotherapy and Oncology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
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25
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Owais M, Yoon HS, Mahmood T, Haider A, Sultan H, Park KR. Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database. Appl Soft Comput 2021; 108:107490. [PMID: 33994894 PMCID: PMC8103783 DOI: 10.1016/j.asoc.2021.107490] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 04/14/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022]
Abstract
Currently, the coronavirus disease 2019 (COVID19) pandemic has killed more than one million people worldwide. In the present outbreak, radiological imaging modalities such as computed tomography (CT) and X-rays are being used to diagnose this disease, particularly in the early stage. However, the assessment of radiographic images includes a subjective evaluation that is time-consuming and requires substantial clinical skills. Nevertheless, the recent evolution in artificial intelligence (AI) has further strengthened the ability of computer-aided diagnosis tools and supported medical professionals in making effective diagnostic decisions. Therefore, in this study, the strength of various AI algorithms was analyzed to diagnose COVID19 infection from large-scale radiographic datasets. Based on this analysis, a light-weighted deep network is proposed, which is the first ensemble design (based on MobileNet, ShuffleNet, and FCNet) in medical domain (particularly for COVID19 diagnosis) that encompasses the reduced number of trainable parameters (a total of 3.16 million parameters) and outperforms the various existing models. Moreover, the addition of a multilevel activation visualization layer in the proposed network further visualizes the lesion patterns as multilevel class activation maps (ML-CAMs) along with the diagnostic result (either COVID19 positive or negative). Such additional output as ML-CAMs provides a visual insight of the computer decision and may assist radiologists in validating it, particularly in uncertain situations Additionally, a novel hierarchical training procedure was adopted to perform the training of the proposed network. It proceeds the network training by the adaptive number of epochs based on the validation dataset rather than using the fixed number of epochs. The quantitative results show the better performance of the proposed training method over the conventional end-to-end training procedure. A large collection of CT-scan and X-ray datasets (based on six publicly available datasets) was used to evaluate the performance of the proposed model and other baseline methods. The experimental results of the proposed network exhibit a promising performance in terms of diagnostic decision. An average F1 score (F1) of 94.60% and 95.94% and area under the curve (AUC) of 97.50% and 97.99% are achieved for the CT-scan and X-ray datasets, respectively. Finally, the detailed comparative analysis reveals that the proposed model outperforms the various state-of-the-art methods in terms of both quantitative and computational performance.
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Affiliation(s)
- Muhammad Owais
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
| | - Hyo Sik Yoon
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
| | - Tahir Mahmood
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
| | - Adnan Haider
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
| | - Haseeb Sultan
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
| | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
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26
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Abdar M, Samami M, Dehghani Mahmoodabad S, Doan T, Mazoure B, Hashemifesharaki R, Liu L, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S. Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Comput Biol Med 2021; 135:104418. [PMID: 34052016 DOI: 10.1016/j.compbiomed.2021.104418] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 04/01/2021] [Accepted: 04/17/2021] [Indexed: 12/18/2022]
Abstract
Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification and segmentation, clearly becoming the state-of-the-art methods. However, most of these methods are unable to provide uncertainty quantification (UQ) for their output, often being overconfident, which can lead to disastrous consequences. Bayesian Deep Learning (BDL) methods can be used to quantify uncertainty of traditional deep learning methods, and thus address this issue. We apply three uncertainty quantification methods to deal with uncertainty during skin cancer image classification. They are as follows: Monte Carlo (MC) dropout, Ensemble MC (EMC) dropout and Deep Ensemble (DE). To further resolve the remaining uncertainty after applying the MC, EMC and DE methods, we describe a novel hybrid dynamic BDL model, taking into account uncertainty, based on the Three-Way Decision (TWD) theory. The proposed dynamic model enables us to use different UQ methods and different deep neural networks in distinct classification phases. So, the elements of each phase can be adjusted according to the dataset under consideration. In this study, two best UQ methods (i.e., DE and EMC) are applied in two classification phases (the first and second phases) to analyze two well-known skin cancer datasets, preventing one from making overconfident decisions when it comes to diagnosing the disease. The accuracy and the F1-score of our final solution are, respectively, 88.95% and 89.00% for the first dataset, and 90.96% and 91.00% for the second dataset. Our results suggest that the proposed TWDBDL model can be used effectively at different stages of medical image analysis.
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Affiliation(s)
- Moloud Abdar
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.
| | - Maryam Samami
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Sajjad Dehghani Mahmoodabad
- Department of Artificial Intelligence, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Thang Doan
- Department of Computer Science, McGill University / Mila, Montreal, Canada
| | - Bogdan Mazoure
- Department of Computer Science, McGill University / Mila, Montreal, Canada
| | | | - Li Liu
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Singapore University of Social Sciences, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Vladimir Makarenkov
- Department of Computer Science, University of Quebec in Montreal, Montreal, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
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27
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Gao L, Zhang L, Liu C, Wu S. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artif Intell Med 2020; 108:101935. [PMID: 32972664 PMCID: PMC7519174 DOI: 10.1016/j.artmed.2020.101935] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/20/2020] [Accepted: 07/17/2020] [Indexed: 11/17/2022]
Abstract
In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods.
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Affiliation(s)
- Long Gao
- College of Computer, National University of Defense Technology, Changsha, 410073, China; Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA.
| | - Lei Zhang
- Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA
| | - Chang Liu
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA
| | - Shandong Wu
- Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Department of Biomedical Informatics, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Intelligent Systems Program, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA.
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28
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Ghasemi M, Kelarestaghi M, Eshghi F, Sharifi A. FDSR: A new fuzzy discriminative sparse representation method for medical image classification. Artif Intell Med 2020; 106:101876. [PMID: 32593393 DOI: 10.1016/j.artmed.2020.101876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/02/2020] [Indexed: 11/22/2022]
Abstract
Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.
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29
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Xin S, Shi H, Jide A, Zhu M, Ma C, Liao H. Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network. Med Biol Eng Comput 2020; 58:659-668. [PMID: 31950330 DOI: 10.1007/s11517-020-02126-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 01/07/2020] [Indexed: 11/24/2022]
Abstract
Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess HE lesions accurately, we propose a novel automatic HE lesion segmentation and classification network that contains lesion region positioning (LRP) and lesion region segmenting (LRS) modules. First, we used the LRP module to obtain the probability map of the lesion distribution and the position of the lesion. Then, based on the result of the LRP module, we used the LRS module to precisely segment the HE lesions within the high-probability region. Finally, we classified the HE lesions and identified the lesion types by a convolutional neural network (CNN). The entire dataset was delineated by the hospital's senior radiologist. We collected CT slices of 160 patients from Qinghai Provincial People's Hospital. The Dice score of the final segmentation result reached 89.89%. The Dice scores, indicating the classification accuracy, for cystic vs. alveolar echinococcosis and calcified vs. noncalcified lesions were 80.32% and 82.45%, the sensitivities were 72.41% and 75.17%, the specificities were 83.72% and 86.04%, the NPVs were 80.01% and 86.96%, the PPVs were 80.45% and 81.74%, and the areas under the ROC curves were 0.8128 and 0.8205, respectively. Graphical abstract.
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Affiliation(s)
- Shenghai Xin
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.,Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Huabei Shi
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - A Jide
- Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Mingyu Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Cong Ma
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
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30
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Talo M. Automated classification of histopathology images using transfer learning. Artif Intell Med 2019; 101:101743. [PMID: 31813483 DOI: 10.1016/j.artmed.2019.101743] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/16/2019] [Accepted: 10/21/2019] [Indexed: 01/23/2023]
Abstract
Early and accurate diagnosis of diseases can often save lives. Diagnosis of diseases from tissue samples is done manually by pathologists. Diagnostics process is usually time consuming and expensive. Hence, automated analysis of tissue samples from histopathology images has critical importance for early diagnosis and treatment. The computer aided systems can improve the quality of diagnoses and give pathologists a second opinion for critical cases. In this study, a deep learning based transfer learning approach has been proposed to classify histopathology images automatically. Two well-known and current pre-trained convolutional neural network (CNN) models, ResNet-50 and DenseNet-161, have been trained and tested using color and grayscale images. The DenseNet-161 tested on grayscale images and obtained the best classification accuracy of 97.89%. Additionally, ResNet-50 pre-trained model was tested on the color images of the Kimia Path24 dataset and achieved the highest classification accuracy of 98.87%. According to the obtained results, it may be said that the proposed pre-trained models can be used for fast and accurate classification of histopathology images and assist pathologists in their daily clinical tasks.
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Affiliation(s)
- Muhammed Talo
- Department of Software Engineering, Firat University, Elazig, Turkey
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31
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Song Y, Cai W, Huang H, Zhou Y, Wang Y, Feng DD. Locality-constrained Subcluster Representation Ensemble for lung image classification. Med Image Anal 2015; 22:102-13. [PMID: 25839422 DOI: 10.1016/j.media.2015.03.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Revised: 03/06/2015] [Accepted: 03/13/2015] [Indexed: 11/30/2022]
Abstract
In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-class variation and inter-class ambiguity in the feature space. Modelling of feature space separation between different classes is thus problematic and this affects the classification performance. Our LSRE model tackles this issue in an ensemble classification construct. The image set is first partitioned into subclusters based on spectral clustering with approximation-based affinity matrix. Basis representations of the test image are then generated with sparse approximation from the subclusters. These basis representations are finally fused with approximation- and distribution-based weights to classify the test image. Our experimental results on a large HRCT database show good performance improvement over existing popular classifiers.
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Affiliation(s)
- Yang Song
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia.
| | - Weidong Cai
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia
| | - Heng Huang
- Department of Computer Science and Engineering, University of Texas, Arlington, TX 76019, USA
| | - Yun Zhou
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - David Dagan Feng
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia
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32
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Gadermayr M, Liedlgruber M, Uhl A, Vécsei A. Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease. Comput Methods Programs Biomed 2013; 112:694-712. [PMID: 23981585 PMCID: PMC3898828 DOI: 10.1016/j.cmpb.2013.07.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Revised: 06/27/2013] [Accepted: 07/02/2013] [Indexed: 06/02/2023]
Abstract
Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification.
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Affiliation(s)
- M. Gadermayr
- Department of Computer Sciences, University of Salzburg, Austria
| | - M. Liedlgruber
- Department of Computer Sciences, University of Salzburg, Austria
| | - A. Uhl
- Department of Computer Sciences, University of Salzburg, Austria
| | - A. Vécsei
- St. Anna Children's Hospital, Department of Pediatrics, Medical University, Vienna, Austria
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