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Zhang J, Guo J, Lu D, Cao Y. ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis. Sci Rep 2024; 14:13696. [PMID: 38871844 DOI: 10.1038/s41598-024-64299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.
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Affiliation(s)
- Jian Zhang
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China.
| | - Jifeng Guo
- College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, 540004, China
| | - Donglei Lu
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
| | - Yuanyuan Cao
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
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2
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Saeedinia SA, Jahed-Motlagh MR, Tafakhori A, Kasabov NK. Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine. Sci Rep 2024; 14:10667. [PMID: 38724576 PMCID: PMC11082192 DOI: 10.1038/s41598-024-60996-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 04/30/2024] [Indexed: 05/12/2024] Open
Abstract
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.
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Affiliation(s)
| | | | - Abbas Tafakhori
- Department of Neurology, School of Medicine, Iranian Center of Neurological Research, Tehran University of Medical Sciences, Tehran, Iran
| | - Nikola Kirilov Kasabov
- School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
- Institute for Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria.
- Computer Science and Engineering Department, Dalian University, Dalian, China.
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3
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Kaifi R. A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification. Diagnostics (Basel) 2023; 13:3007. [PMID: 37761373 PMCID: PMC10527911 DOI: 10.3390/diagnostics13183007] [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: 06/23/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.
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Affiliation(s)
- Reham Kaifi
- Department of Radiological Sciences, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah City 22384, Saudi Arabia;
- King Abdullah International Medical Research Center, Jeddah City 22384, Saudi Arabia
- Medical Imaging Department, Ministry of the National Guard—Health Affairs, Jeddah City 11426, Saudi Arabia
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Ravinder M, Saluja G, Allabun S, Alqahtani MS, Abbas M, Othman M, Soufiene BO. Enhanced brain tumor classification using graph convolutional neural network architecture. Sci Rep 2023; 13:14938. [PMID: 37697022 PMCID: PMC10495443 DOI: 10.1038/s41598-023-41407-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023] Open
Abstract
The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.
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Affiliation(s)
- M Ravinder
- CSE, Indira Gandhi Delhi Technical University for Women, New Delhi, India
| | - Garima Saluja
- CSE, Indira Gandhi Delhi Technical University for Women, New Delhi, India
| | - Sarah Allabun
- Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Manal Othman
- Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, University of Sousse, Hammam Sousse, Tunisia.
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5
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Mathew NA, Stanley IM, Jose R. Machine Learning based tumor diagnosis using compressive sensing in MRI images. Biomed Phys Eng Express 2023; 9:055023. [PMID: 37524065 DOI: 10.1088/2057-1976/acebf1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Despite the widespread use of Magnetic Resonance Imaging (MRI) analysis for disease diagnosis, processing and analyzing the substantial amount of acquired data may be challenging. Compressive Sensing (CS) offers a promising solution to this problem. MRI diagnosis can be performed faster and more accurately using CS since it requires fewer data for image analysis. A combination of CS with conventional and Deep Learning (DL) models, specifically VGGNet-16, is proposed for categorizing reconstructed MRI images into healthy and unhealthy. The model is properly trained using a dataset containing both normal and tumor images. The method is evaluated using a variety of parameters, including recall, F1-score, accuracy, and precision. Using the VGGNet-16 model, the proposed work achieved a classification accuracy of 98.7%, which is comparable with another state-of-the-art method based on traditionally acquired MRI images. The results indicate that CS may be useful in clinical settings for improving the efficiency and accuracy of MRI-based tumor diagnosis. Furthermore, the approach could be extended to other medical imaging modalities, possibly improving diagnosis accuracy. The study illustrates how CS can enhance medical imaging analysis, particularly in the context of tumor diagnosis using MRI images. It is necessary to conduct further research to investigate the potential applications of CS in other medical imaging contexts.
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Affiliation(s)
- Nimmy Ann Mathew
- Department of Electronics and Communication, Rajiv Gandhi Institute of Technology, Kottayam, Kerala, 686501. Affiliated to APJ Abdul Kalam Technological University, Kerala, India
| | - Ishita Maria Stanley
- Department of Electronics and Communication, Rajiv Gandhi Institute of Technology, Kottayam, Kerala, 686501. Affiliated to APJ Abdul Kalam Technological University, Kerala, India
| | - Renu Jose
- Department of Electronics and Communication, Rajiv Gandhi Institute of Technology, Kottayam, Kerala, 686501. Affiliated to APJ Abdul Kalam Technological University, Kerala, India
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Li X, Zhu Z, Yin H, Zhao P, Lv H, Tang R, Qin Y, Zhuo L, Wang Z. Labyrinth morphological modeling and its application on unreferenced segmentation assessment. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Ullah Z, Usman M, Gwak J. MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 216:119475. [PMID: 36619348 PMCID: PMC9810379 DOI: 10.1016/j.eswa.2022.119475] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/28/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
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8
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Reddy KR, Batchu RK, Polinati S, Bavirisetti DP. Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features. Front Hum Neurosci 2023; 17:1157155. [PMID: 37033909 PMCID: PMC10073563 DOI: 10.3389/fnhum.2023.1157155] [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: 02/02/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Brain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient's life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm. Methods This paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB). Results The presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models. Discussions By analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.
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Affiliation(s)
- K. Rasool Reddy
- Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology (MRCET), Hyderabad, India
| | - Raj Kumar Batchu
- Department of Computer Science and Engineering (Data Science), Prasad V. Potluri Siddhartha Institute of Technology (PVPSIT), Vijayawada, India
| | - Srinivasu Polinati
- Department of Electronics and Communication Engineering, Vignan’s Institute of Engineering for Women (VIEW), Visakhapatnam, India
| | - Durga Prasad Bavirisetti
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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9
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Khmila H, Kallel IK, Bossé E, Solaiman B. An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS. ENTROPY (BASEL, SWITZERLAND) 2023; 25:529. [PMID: 36981418 PMCID: PMC10048720 DOI: 10.3390/e25030529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/05/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.
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Affiliation(s)
- Houda Khmila
- Control and Energy Management (CEM Lab), Sfax Engineering School, University of Sfax, BP W, Sfax 3038, Tunisia
- Smart Aid Technologies SATECH, Sfax 3061, Tunisia
| | - Imene Khanfir Kallel
- Control and Energy Management (CEM Lab), Sfax Engineering School, University of Sfax, BP W, Sfax 3038, Tunisia
- Smart Aid Technologies SATECH, Sfax 3061, Tunisia
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, CEDEX, 29238 Brest, France
| | - Eloi Bossé
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, CEDEX, 29238 Brest, France
- Expertises Parafuse Inc., Quebec, QC G1W 4N1, Canada
| | - Basel Solaiman
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, CEDEX, 29238 Brest, France
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Srinivasan S, Bai PSM, Mathivanan SK, Muthukumaran V, Babu JC, Vilcekova L. Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique. Diagnostics (Basel) 2023; 13:diagnostics13061153. [PMID: 36980463 PMCID: PMC10046932 DOI: 10.3390/diagnostics13061153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/14/2023] [Accepted: 03/14/2023] [Indexed: 03/22/2023] Open
Abstract
To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.
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Affiliation(s)
- Saravanan Srinivasan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
| | | | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Venkatesan Muthukumaran
- Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Jyothi Chinna Babu
- Department of Electronics and Communications Engineering, Annamacharya Institute of Technology and Sciences, Rajampet 516126, India
| | - Lucia Vilcekova
- Faculty of Management, Comenius University Bratislava, Odbojarov 10, 820 05 Bratislava, Slovakia
- Correspondence:
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An Ensemble Model for the Diagnosis of Brain Tumors through MRIs. Diagnostics (Basel) 2023; 13:diagnostics13030561. [PMID: 36766666 PMCID: PMC9913902 DOI: 10.3390/diagnostics13030561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/14/2023] [Accepted: 01/15/2023] [Indexed: 02/05/2023] Open
Abstract
Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.
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Gusmanov A, Zhakhina G, Yerdessov S, Sakko Y, Mussina K, Alimbayev A, Syssoyev D, Sarria-Santamera A, Gaipov A. Review of the research databases on population-based Registries of Unified electronic Healthcare system of Kazakhstan (UNEHS): Possibilities and limitations for epidemiological research and Real-World Evidence. Int J Med Inform 2023; 170:104950. [PMID: 36508752 DOI: 10.1016/j.ijmedinf.2022.104950] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION A 'learning healthcare system', based on electronic health records and other routinely collected healthcare data, would allow Real World Data (RWD) to be continuously fed into the system, ensuring that with every new patient treated, we know more overall about the practice of medicine. A judicious use of RWD would complement the traditional evidence from clinical research, for the benefit of all stakeholders involved in healthcare. Lack of data on disease epidemiology in Kazakhstan resonates with lower life expectancy and poorer health indicators compared to countries with analogous income per capita. Usage of primary data collection methods to fill these gaps require additional financial and human resources. Usage of big data, which is routinely collected though healthcare information systems, is considered as a competitive alternative in described circumstances. OBJECTIVE Development of the Unified National Electronic Healthcare System (UNEHS) in Kazakhstan allowed the creation of research databases to investigate epidemiology of numerous diseases. UNEHS research databases endorse extensive research activities due to a prospective follow-up, coverage of the whole Kazakhstani population and relatively lower expenses to conduct epidemiological studies. This review paper aims to introduce the content and descriptive data on research databases on population-based registries of UNEHS and to discuss opportunities and limitations of its usage. RESULTS AND DISCUSSION UNEHS databases include medical data on 36.4% of an adult population of Kazakhstan. Research databases presented in this paper contain critical variables that can be utilized for investigation of disease epidemiology, effectiveness of provided medical procedures and infectious disease epidemiology. A few examples accompany a detailed elaboration on the possibilities of research database utilization in epidemiological research. CONCLUSION Considering numerous advantages, the UNEHS research databases are expected to greatly contribute to healthcare in Kazakhstan by providing critical data on disease epidemiology. To warrant long-term usage and high research output several concerns and limitations should be addressed as well.
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Affiliation(s)
- Arnur Gusmanov
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Gulnur Zhakhina
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Sauran Yerdessov
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Yesbolat Sakko
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Kamilla Mussina
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Aidar Alimbayev
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Dmitriy Syssoyev
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Antonio Sarria-Santamera
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan
| | - Abduzhappar Gaipov
- Department of Medicine, Nazarbayev University School of Medicine, Kerey and Zhanibek Khans Street 5/1, Astana, Kazakhstan.
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Wang C, Zhang L, Zhang J, Qiao L, Liu M. Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification. J Pers Med 2023; 13:jpm13020251. [PMID: 36836485 PMCID: PMC9958959 DOI: 10.3390/jpm13020251] [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: 11/12/2022] [Revised: 12/27/2022] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson's correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN "features" that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality.
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Affiliation(s)
- Chengcheng Wang
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
- Correspondence: (L.Z.); (M.L.)
| | - Jinshan Zhang
- College of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (L.Z.); (M.L.)
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14
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Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review. Cancers (Basel) 2023; 15:cancers15030837. [PMID: 36765794 PMCID: PMC9913672 DOI: 10.3390/cancers15030837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023] Open
Abstract
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.
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15
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Ullah Z, Usman M, Latif S, Gwak J. Densely attention mechanism based network for COVID-19 detection in chest X-rays. Sci Rep 2023; 13:261. [PMID: 36609667 PMCID: PMC9816547 DOI: 10.1038/s41598-022-27266-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Siddique Latif
- Faculty of Health and Computing, University of Southern Queensland, Toowoomba, QLD, 4300, Australia
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of IT. Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, 27469, South Korea.
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16
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Saladi S, Karuna Y, Koppu S, Reddy GR, Mohan S, Mallik S, Qin H. Segmentation and Analysis Emphasizing Neonatal MRI Brain Images Using Machine Learning Techniques. MATHEMATICS 2023; 11:285. [DOI: 10.3390/math11020285] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to avoid miscues in results. This paper is divided into two parts: In the first half, preprocessing was accomplished using HE, CLAHE, and BPDFHE enhancement techniques. An analysis is the sequel to the above methods to check for the best method based on performance metrics, i.e., MSE, PSNR, RMSE, and AMBE. The second half deals with the segmentation process. We propose a novel ARKFCM to use for segmentation. Finally, the trends in the performance metrics (dice similarity and Jaccard similarity) as well as the segmentation results are discussed in comparison with the conventional FCM method.
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17
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Balaha HM, Hassan AES. A variate brain tumor segmentation, optimization, and recognition framework. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10337-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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18
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Haq EU, Jianjun H, Huarong X, Li K, Weng L. A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6446680. [PMID: 36035291 PMCID: PMC9400402 DOI: 10.1155/2022/6446680] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 11/17/2022]
Abstract
Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model's performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.
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Affiliation(s)
- Ejaz Ul Haq
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
- School of Computer and Information Engineering, Xiamen University of Technology, China
| | - Huang Jianjun
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
| | - Xu Huarong
- School of Computer and Information Engineering, Xiamen University of Technology, China
| | - Kang Li
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
| | - Lifen Weng
- School of Computer and Information Engineering, Xiamen University of Technology, China
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19
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Abstract
Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.
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20
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An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/2092985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This paper proposes a system to effectively identify brain tumors on MRI images using artificial intelligence algorithms and ADAS optimization function. This system is developed with the aim of assisting doctors in diagnosing one of the most dangerous diseases for humans. The data used in the study is patient image data collected from Bach Mai Hospital, Vietnam. The proposed approach includes two main steps. First, we propose the normalization method for brain MRI images to remove unnecessary components without affecting their information content. In the next step, Deep Convolutional Neural Networks are used and then we propose to apply ADAS optimization function to build predictive models based on that normalized dataset. From there, the results will be compared to choose the most optimal method. Those results of the evaluated algorithms through the coefficient F1-score are greater than 94% and the highest value is 97.65%.
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21
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Premalatha R, Dhanalakshmi P. Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach. Neural Comput Appl 2022; 34:11553-11569. [PMID: 35250182 PMCID: PMC8889401 DOI: 10.1007/s00521-022-07043-5] [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: 07/07/2021] [Accepted: 01/30/2022] [Indexed: 11/16/2022]
Abstract
Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values.
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22
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Bansal T, Jindal N. An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06929-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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23
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Mehrrotraa R, Ansari MA, Agrawal R, Tripathi P, Bin Heyat MB, Al-Sarem M, Muaad AYM, Nagmeldin WAE, Abdelmaboud A, Saeed F. Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography. IEEE ACCESS 2022; 10:85442-85458. [DOI: 10.1109/access.2022.3194152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Rajat Mehrrotraa
- Department of Electrical and Electronics Engineering, G. L. Bajaj Institute of Technology & Management, Greater Noida, India
| | - M. A. Ansari
- Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Greater Noida, India
| | - Rajeev Agrawal
- Department of Computer Science, Lloyd Institute of Engineering & Technology, Greater Noida, India
| | - Pragati Tripathi
- Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Greater Noida, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Mohammed Al-Sarem
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | | | - Wamda Abdelrahman Elhag Nagmeldin
- Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Faisal Saeed
- Department of Computing and Data Science, DAAI Research Group, School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K
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24
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Guan Y, Aamir M, Rahman Z, Ali A, Abro WA, Dayo ZA, Bhutta MS, Hu Z. A framework for efficient brain tumor classification using MRI images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5790-5815. [PMID: 34517512 DOI: 10.3934/mbe.2021292] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.
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Affiliation(s)
- Yurong Guan
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
| | - Muhammad Aamir
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
| | - Ziaur Rahman
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
| | - Ammara Ali
- Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway
| | - Waheed Ahmed Abro
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
| | - Zaheer Ahmed Dayo
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
| | - Muhammad Shoaib Bhutta
- Binjiang College, Nanjing University of Information Science & Technology, Wuxi 214105, China
| | - Zhihua Hu
- Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China
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25
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Mishro PK, Agrawal S, Panda R, Abraham A. A novel brightness preserving joint histogram equalization technique for contrast enhancement of brain MR images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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Kang J, Ullah Z, Gwak J. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. SENSORS 2021; 21:s21062222. [PMID: 33810176 PMCID: PMC8004778 DOI: 10.3390/s21062222] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022]
Abstract
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Affiliation(s)
- Jaeyong Kang
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT Convergence (Brain Korea PLUS 21), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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27
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Brain tumor classification in magnetic resonance image using hard swish-based RELU activation function-convolutional neural network. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05671-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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28
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Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186296] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, n = 77; Grade III, n = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.
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