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Huan J, Yuan J, Zhang H, Xu X, Shi B, Zheng Y, Li X, Zhang C, Hu Q, Fan Y, Lv J, Zhou L. Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2024; 89:1961-1980. [PMID: 38678402 DOI: 10.2166/wst.2024.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/02/2024] [Indexed: 04/30/2024]
Abstract
Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.
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Affiliation(s)
- Juan Huan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China E-mail:
| | - Jialong Yuan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Hao Zhang
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Xiangen Xu
- Changzhou Environmental Science Research Institute, Changzhou 213002, China
| | - Bing Shi
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Yongchun Zheng
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Xincheng Li
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Chen Zhang
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Qucheng Hu
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Yixiong Fan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Jiapeng Lv
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Liwan Zhou
- Changzhou Environmental Science Research Institute, Changzhou 213002, China
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2
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Chen W, Tan X, Zhang J, Du G, Fu Q, Jiang H. A robust approach for multi-type classification of brain tumor using deep feature fusion. Front Neurosci 2024; 18:1288274. [PMID: 38440396 PMCID: PMC10909817 DOI: 10.3389/fnins.2024.1288274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/05/2024] [Indexed: 03/06/2024] Open
Abstract
Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients' lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.
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Affiliation(s)
- Wenna Chen
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Xinghua Tan
- College of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jincan Zhang
- College of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Ganqin Du
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Qizhi Fu
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Hongwei Jiang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
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3
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Pitarch C, Ungan G, Julià-Sapé M, Vellido A. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers (Basel) 2024; 16:300. [PMID: 38254790 PMCID: PMC10814384 DOI: 10.3390/cancers16020300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
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Affiliation(s)
- Carla Pitarch
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Eurecat, Digital Health Unit, Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Gulnur Ungan
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Margarida Julià-Sapé
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Alfredo Vellido
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
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4
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Zhang J, Tan X, Chen W, Du G, Fu Q, Zhang H, Jiang H. EFF_D_SVM: a robust multi-type brain tumor classification system. Front Neurosci 2023; 17:1269100. [PMID: 37841686 PMCID: PMC10570803 DOI: 10.3389/fnins.2023.1269100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 08/29/2023] [Indexed: 10/17/2023] Open
Abstract
Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.
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Affiliation(s)
- Jincan Zhang
- College of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Xinghua Tan
- College of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Wenna Chen
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Ganqin Du
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Qizhi Fu
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Hongri Zhang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Hongwei Jiang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
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5
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Chaki J, Woźniak M. A deep learning based four-fold approach to classify brain MRI: BTSCNet. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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6
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Özbay E, Altunbey Özbay F. Interpretable features fusion with precision MRI images deep hashing for brain tumor detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107387. [PMID: 36738605 DOI: 10.1016/j.cmpb.2023.107387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/30/2022] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Brain tumor is a deadly disease that can affect people of all ages. Radiologists play a critical role in the early diagnosis and treatment of the 14,000 persons diagnosed with brain tumors on average each year. The best method for tumor detection with computer-aided diagnosis systems (CADs) is Magnetic Resonance Imaging (MRI). However, manual evaluation using conventional approaches may result in a number of inaccuracies due to the complicated tissue properties of a large number of images. Therefore a precision medical image hashing approach is proposed that combines interpretability and feature fusion using MRI images of brain tumors, to address the issue of medical image retrieval. METHODS A precision hashing method combining interpretability and feature fusion is proposed to recover the problem of low image resolutions in brain tumor detection on the Brain-Tumor-MRI (BT-MRI) dataset. First, the dataset is pre-trained with the DenseNet201 network using the Comparison-to-Learn method. Then, a global network is created that generates the salience map to yield a mask crop with local region discrimination. Finally, the local network features inputs and public features expressing the local discriminant regions are concatenated for the pooling layer. A hash layer is added between the fully connected layer and the classification layer of the backbone network to generate high-quality hash codes. The final result is obtained by calculating the hash codes with the similarity metric. RESULTS Experimental results with the BT-MRI dataset showed that the proposed method can effectively identify tumor regions and more accurate hash codes can be generated by using the three loss functions in feature fusion. It has been demonstrated that the accuracy of medical image retrieval is effectively increased when our method is compared with existing image retrieval approaches. CONCLUSIONS Our method has demonstrated that the accuracy of medical image retrieval can be effectively increased and potentially applied to CADs.
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Affiliation(s)
- Erdal Özbay
- Firat University, Faculty of Engineering, Computer Engineering, 23119, Elazig, Turkey.
| | - Feyza Altunbey Özbay
- Firat University, Faculty of Engineering, Software Engineering, 23119, Elazig, Turkey
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7
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Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet. Diagnostics (Basel) 2023; 13:diagnostics13050859. [PMID: 36900004 PMCID: PMC10000758 DOI: 10.3390/diagnostics13050859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/09/2023] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately using XAI methods. In this study, we preferred datasets that are widely used in the literature, such as the four-class kaggle brain tumor dataset (Dataset I) and the three-class figshare brain tumor dataset (Dataset II). To extract features, a pre-trained deep learning model is chosen. DenseNet201 is used as the feature extractor in this case. The proposed automated brain tumor detection model includes five stages. First, training of brain MR images with DenseNet201, the tumor area was segmented with GradCAM. The features were extracted from DenseNet201 trained using the exemplar method. Extracted features were selected with iterative neighborhood component (INCA) feature selector. Finally, the selected features were classified using support vector machine (SVM) with 10-fold cross-validation. An accuracy of 98.65% and 99.97%, were obtained for Datasets I and II, respectively. The proposed model obtained higher performance than the state-of-the-art methods and can be used to aid radiologists in their diagnosis.
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8
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Saravanan S, Kumar VV, Sarveshwaran V, Indirajithu A, Elangovan D, Allayear SM. Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4380901. [PMID: 36277002 PMCID: PMC9586767 DOI: 10.1155/2022/4380901] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 09/29/2023]
Abstract
The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.
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Affiliation(s)
- S. Saravanan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - V. Vinoth Kumar
- Department of Computer Science and Engineering, Jain (Deemed to Be University), Bangalore, India
| | - Velliangiri Sarveshwaran
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India
| | - Alagiri Indirajithu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014 Tamil Nadu, India
| | - D. Elangovan
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Shaikh Muhammad Allayear
- Department of Multimedia and Creative Technology, Daffodil International University, Daffodil Smart City, Khagan, Ashulia, Dhaka, Bangladesh
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9
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Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5625757. [PMID: 36156956 PMCID: PMC9499747 DOI: 10.1155/2022/5625757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/01/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022]
Abstract
The development of unusual cells in the cerebrum causes brain cancer. It is classified primarily into two classes: a noncarcinogenic (benign) type of growth and cancerous (malignant) growth. Early detection of this disease is a quintessential task for all medical practice professionals. For traditional approaches of tumor detections, certain limitations exist. They include less effectiveness, inability to detect due to low-quality processing of images, less dataset for training and testing, less predictive nature to models, and skipping of quintessential stages. All these lead to inaccurate results of tumor detections. To overcome this issue, this paper brings an effective deep learning technique for brain tumor detection with the following stages: (a) data collection from REMBRANDT dataset containing multisequence MRI of 130 patients; (b) preprocessing using conversion to greyscale, skull stripping, and histogram equalization; (c) segmentation uses genetic algorithm; (d) feature extraction using discrete wavelet transform (DWT); (e) particle swarm optimization technique for feature selection; (f) classification using U-Net. Experiment evaluation states that the proposed model (GA-UNET) outperforms (accuracy: 0.97, sensitivity: 0.98, specificity: 0.98) compared to other advanced models.
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10
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An attention-guided convolutional neural network for automated classification of brain tumor from MRI. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07742-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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Xie Y, Zaccagna F, Rundo L, Testa C, Agati R, Lodi R, Manners DN, Tonon C. Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. Diagnostics (Basel) 2022; 12:diagnostics12081850. [PMID: 36010200 PMCID: PMC9406354 DOI: 10.3390/diagnostics12081850] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.
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Affiliation(s)
- Yuting Xie
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;
| | - Claudia Testa
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Raffaele Agati
- Programma Neuroradiologia con Tecniche ad elevata complessità, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy
| | - David Neil Manners
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Correspondence:
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
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12
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Wang XY, Li C, Zhang R, Wang L, Tan JL, Wang H. Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform. Front Neurosci 2022; 16:921642. [PMID: 35720691 PMCID: PMC9198366 DOI: 10.3389/fnins.2022.921642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 05/09/2022] [Indexed: 11/23/2022] Open
Abstract
At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications.
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Affiliation(s)
- Xian-Yu Wang
- State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China.,Academy of Space Electronic Information Technology, Xi'an, China
| | - Cong Li
- Academy of Space Electronic Information Technology, Xi'an, China
| | - Rui Zhang
- Shaanxi Academy of Aerospace Technology Application Co., Ltd., Xi'an, China
| | - Liang Wang
- Shaanxi Academy of Aerospace Technology Application Co., Ltd., Xi'an, China
| | - Jin-Lin Tan
- Shaanxi Academy of Aerospace Technology Application Co., Ltd., Xi'an, China.,School of Aerospace Science and Technology, Xidian University, Xi'an, China
| | - Hai Wang
- School of Aerospace Science and Technology, Xidian University, Xi'an, China
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13
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Siddiqi MH, Alsayat A, Alhwaiti Y, Azad M, Alruwaili M, Alanazi S, Kamruzzaman MM, Khan A. A Precise Medical Imaging Approach for Brain MRI Image Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6447769. [PMID: 35548099 PMCID: PMC9085323 DOI: 10.1155/2022/6447769] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/12/2022] [Indexed: 11/22/2022]
Abstract
Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.
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Affiliation(s)
| | - Ahmed Alsayat
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - Yousef Alhwaiti
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - Mohammad Azad
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - Madallah Alruwaili
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - Saad Alanazi
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - M. M. Kamruzzaman
- College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, Saudi Arabia
| | - Asfandyar Khan
- Institute of Computer Science & IT, The University of Agriculture Peshawar, Peshawar, Pakistan
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14
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Clever Hans effect found in a widely used brain tumour MRI dataset. Med Image Anal 2022; 77:102368. [DOI: 10.1016/j.media.2022.102368] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 12/19/2021] [Accepted: 01/10/2022] [Indexed: 12/11/2022]
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15
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Veeramuthu A, Meenakshi S, Mathivanan G, Kotecha K, Saini JR, Vijayakumar V, Subramaniyaswamy V. MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier. Front Psychol 2022; 13:848784. [PMID: 35310201 PMCID: PMC8931531 DOI: 10.3389/fpsyg.2022.848784] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/19/2022] [Indexed: 11/17/2022] Open
Abstract
Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks (DCNN)-based architectures are proposed for image classification, namely, actual image feature-based classifier (AIFC), segmented image feature-based classifier (SIFC), actual and segmented image feature-based classifier (ASIFC), actual image-based classifier (AIC), segmented image-based classifier (SIC), actual and segmented image-based classifier (ASIC), and finally, CFIC. The Kaggle Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods.
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Affiliation(s)
- A. Veeramuthu
- Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
| | - S. Meenakshi
- Department of Information Technology, Jeppiaar SRR Engineering College, Chennai, India
| | - G. Mathivanan
- Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India
- *Correspondence: Ketan Kotecha,
| | - Jatinderkumar R. Saini
- Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India
| | - V. Vijayakumar
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - V. Subramaniyaswamy
- School of Computing, Shanmugha Arts, Science, Technology & Research Academy Deemed University, Thanjavur, India
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16
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Zhou G, Lu B, Hu X, Ni T. Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval. Front Neurosci 2022; 15:829040. [PMID: 35095411 PMCID: PMC8795867 DOI: 10.3389/fnins.2021.829040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/27/2021] [Indexed: 12/20/2022] Open
Abstract
Magnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images from a huge database is of great significance to doctors’ auxiliary diagnosis and treatment. In this paper, combining the advantages of sparse representation and metric learning, a sparse representation-based discriminative metric learning (SRDML) approach is proposed for medical image retrieval of brain MRI. The SRDML approach uses a sparse representation framework to learn robust feature representation of brain MRI, and uses metric learning to project new features into the metric space with matching discrimination. In such a metric space, the optimal similarity measure is obtained by using the local constraints of atoms and the pairwise constraints of coding coefficients, so that the distance between similar images is less than the given threshold, and the distance between dissimilar images is greater than another given threshold. The experiments are designed and tested on the brain MRI dataset created by Chang. Experimental results show that the SRDML approach can obtain satisfactory retrieval performance and achieve accurate brain MRI image retrieval.
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Affiliation(s)
- Guohua Zhou
- School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- College of Information Engineering, Yangzhou University, Yangzhou, China
| | - Bing Lu
- School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, China
| | - Xuelong Hu
- College of Information Engineering, Yangzhou University, Yangzhou, China
| | - Tongguang Ni
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- *Correspondence: Tongguang Ni,
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17
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Xiao G, Wang H, Shen J, Chen Z, Zhang Z, Ge X. Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI. MICROMACHINES 2021; 13:mi13010015. [PMID: 35056179 PMCID: PMC8780069 DOI: 10.3390/mi13010015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/15/2021] [Accepted: 12/20/2021] [Indexed: 12/30/2022]
Abstract
Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.
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Affiliation(s)
- Guanghua Xiao
- College of Computer and Information Engineering, Hohai University, Nanjing 211100, China; (G.X.); (J.S.); (Z.C.); (Z.Z.)
- Department of Equipment Engineering, Jiangsu Urban and Rural Construction College, Changzhou 213147, China
| | - Huibin Wang
- College of Computer and Information Engineering, Hohai University, Nanjing 211100, China; (G.X.); (J.S.); (Z.C.); (Z.Z.)
- Correspondence:
| | - Jie Shen
- College of Computer and Information Engineering, Hohai University, Nanjing 211100, China; (G.X.); (J.S.); (Z.C.); (Z.Z.)
| | - Zhe Chen
- College of Computer and Information Engineering, Hohai University, Nanjing 211100, China; (G.X.); (J.S.); (Z.C.); (Z.Z.)
| | - Zhen Zhang
- College of Computer and Information Engineering, Hohai University, Nanjing 211100, China; (G.X.); (J.S.); (Z.C.); (Z.Z.)
| | - Xiaomin Ge
- Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou 213000, China;
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18
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Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021; 64:647-668. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.
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