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Yurtsever MME, Atay Y, Arslan B, Sagiroglu S. Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset. BMC Med Inform Decis Mak 2024; 24:285. [PMID: 39367444 PMCID: PMC11450983 DOI: 10.1186/s12911-024-02699-6] [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: 04/17/2024] [Accepted: 09/26/2024] [Indexed: 10/06/2024] Open
Abstract
Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets.
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Affiliation(s)
- M M Enes Yurtsever
- Information Systems Engineering, Kocaeli University, Kocaeli, 41001, Türkiye
| | - Yilmaz Atay
- Computer Engineering, Engineering Faculty, Gazi University, Ankara, 06570, Türkiye.
| | - Bilgehan Arslan
- Computer Engineering, Engineering Faculty, Gazi University, Ankara, 06570, Türkiye
| | - Seref Sagiroglu
- Computer Engineering, Engineering Faculty, Gazi University, Ankara, 06570, Türkiye
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Ullah MS, Khan MA, Masood A, Mzoughi O, Saidani O, Alturki N. Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm. Front Oncol 2024; 14:1335740. [PMID: 38390266 PMCID: PMC10882068 DOI: 10.3389/fonc.2024.1335740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/12/2024] [Indexed: 02/24/2024] Open
Abstract
Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.
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Affiliation(s)
| | | | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Olfa Mzoughi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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3
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Cai Z, Wong LM, Wong YH, Lee HL, Li KY, So TY. Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading. Cancers (Basel) 2023; 15:5459. [PMID: 38001719 PMCID: PMC10670283 DOI: 10.3390/cancers15225459] [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: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). METHODS This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. RESULTS The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. CONCLUSIONS The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.
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Affiliation(s)
| | | | | | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China; (Z.C.); (L.M.W.); (Y.H.W.); (H.-l.L.); (K.-y.L.)
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Emam MM, Samee NA, Jamjoom MM, Houssein EH. Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm. Comput Biol Med 2023; 160:106966. [PMID: 37141655 DOI: 10.1016/j.compbiomed.2023.106966] [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: 02/10/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 05/06/2023]
Abstract
One of the worst diseases is a brain tumor, which is defined by abnormal development of synapses in the brain. Early detection of brain tumors is essential for improving prognosis, and classifying tumors is a vital step in the disease's treatment. Different classification strategies using deep learning have been presented for the diagnosis of brain tumors. However, several challenges exist, such as the need for a competent specialist in classifying brain cancers by deep learning models and the problem of building the most precise deep learning model for categorizing brain tumors. We propose an evolved and highly efficient model based on deep learning and improved metaheuristic algorithms to address these challenges. Specifically, we develop an optimized residual learning architecture for classifying multiple brain tumors and propose an improved variant of the Hunger Games Search algorithm (I-HGS) based on combining two enhancing strategies: Local Escaping Operator (LEO) and Brownian motion. These two strategies balance solution diversity and convergence speed, boosting the optimization performance and staying away from the local optima. First, we have evaluated the I-HGS algorithm on the IEEE Congress on Evolutionary Computation held in 2020 (CEC'2020) test functions, demonstrating that I-HGS outperformed the basic HGS and other popular algorithms regarding statistical convergence, and various measures. The suggested model is then applied to the optimization of the hyperparameters of the Residual Network 50 (ResNet50) model (I-HGS-ResNet50) for brain cancer identification, proving its overall efficacy. We utilize several publicly available, gold-standard datasets of brain MRI images. The proposed I-HGS-ResNet50 model is compared with other existing studies as well as with other deep learning architectures, including Visual Geometry Group 16-layer (VGG16), MobileNet, and Densely Connected Convolutional Network 201 (DenseNet201). The experiments demonstrated that the proposed I-HGS-ResNet50 model surpasses the previous studies and other well-known deep learning models. I-HGS-ResNet50 acquired an accuracy of 99.89%, 99.72%, and 99.88% for the three datasets. These results efficiently prove the potential of the proposed I-HGS-ResNet50 model for accurate brain tumor classification.
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Affiliation(s)
- Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Mona M Jamjoom
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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Alrumiah SS, Alrebdi N, Ibrahim DM. Augmenting healthy brain magnetic resonance images using generative adversarial networks. PeerJ Comput Sci 2023; 9:e1318. [PMID: 37346635 PMCID: PMC10280481 DOI: 10.7717/peerj-cs.1318] [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: 02/17/2022] [Accepted: 03/09/2023] [Indexed: 06/23/2023]
Abstract
Machine learning applications in the medical sector face a lack of medical data due to privacy issues. For instance, brain tumor image-based classification suffers from the lack of brain images. The lack of such images produces some classification problems, i.e., class imbalance issues which can cause a bias toward one class over the others. This study aims to solve the imbalance problem of the "no tumor" class in the publicly available brain magnetic resonance imaging (MRI) dataset. Generative adversarial network (GAN)-based augmentation techniques were used to solve the imbalance classification problem. Specifically, deep convolutional GAN (DCGAN) and single GAN (SinGAN). Moreover, the traditional-based augmentation techniques were implemented using the rotation method. Thus, several VGG16 classification experiments were conducted, including (i) the original dataset, (ii) the DCGAN-based dataset, (iii) the SinGAN-based dataset, (iv) a combination of the DCGAN and SinGAN dataset, and (v) the rotation-based dataset. However, the results show that the original dataset achieved the highest accuracy, 73%. Additionally, SinGAN outperformed DCGAN by a significant margin of 4%. In contrast, experimenting with the non-augmented original dataset resulted in the highest classification loss value, which explains the effect of the imbalance issue. These results provide a general view of the effect of different image augmentation techniques on enlarging the healthy brain dataset.
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Affiliation(s)
- Sarah S. Alrumiah
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Norah Alrebdi
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Dina M. Ibrahim
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
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Chughtai IT, Naseer A, Tamoor M, Asif S, Jabbar M, Shahid R. Content-based image retrieval via transfer learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
In the past few years, due to the increased usage of internet, smartphones, sensors and digital cameras, more than a million images are generated and uploaded daily on social media platforms. The massive generation of such multimedia contents has resulted in an exponential growth in the stored and shared data. Certain ever-growing image repositories, consisting of medical images, satellites images, surveillance footages, military reconnaissance, fingerprints and scientific data etc., has increased the motivation for developing robust and efficient search methods for image retrieval as per user requirements. Hence, it is need of the hour to search and retrieve relevant images efficiently and with good accuracy. The current research focuses on Content-based Image Retrieval (CBIR) and explores well-known transfer learning-based classifiers such as VGG16, VGG19, EfficientNetB0, ResNet50 and their variants. These deep transfer leaners are trained on three benchmark image datasets i.e., CIFAR-10, CIFAR-100 and CINIC-10 containing 10, 100, and 10 classes respectively. In total 16 customized models are evaluated on these benchmark datasets and 96% accuracy is achieved for CIFAR-10 while 83% accuracy is achieved for CIFAR-100.
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Affiliation(s)
| | - Asma Naseer
- National, University of Computer and Emerging Sciences, Lahore, Pakistan
| | | | - Saara Asif
- Technische Hochschule Ingolstadt, Germany
| | | | - Rabia Shahid
- Government College University, Faisalabad, Pakistan
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Raza N, Naseer A, Tamoor M, Zafar K. Alzheimer Disease Classification through Transfer Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13040801. [PMID: 36832292 PMCID: PMC9955379 DOI: 10.3390/diagnostics13040801] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Alzheimer's disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer's disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer's disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.
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Affiliation(s)
- Noman Raza
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan
| | - Kashif Zafar
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
- Correspondence:
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Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi S, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med 2023; 152:106405. [PMID: 36512875 DOI: 10.1016/j.compbiomed.2022.106405] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/06/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. METHODS The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. RESULTS Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. CONCLUSION The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.
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Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | | | | | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
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Ur Rehman A, Naseer A, Karim S, Tamoor M, Naz S. Deep learning classifiers for computer-aided diagnosis of multiple lungs disease. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1125-1143. [PMID: 37522236 DOI: 10.3233/xst-230113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
BACKGROUND Computer aided diagnosis has gained momentum in the recent past. The advances in deep learning and availability of huge volumes of data along with increased computational capabilities has reshaped the diagnosis and prognosis procedures. OBJECTIVE These methods are proven to be relatively less expensive and safer alternatives of the otherwise traditional approaches. This study is focused on efficient diagnosis of three very common diseases: lung cancer, pneumonia and Covid-19 using X-ray images. METHODS Three different deep learning models are designed and developed to perform 4-way classification. Inception V3, Convolutional Neural Networks (CNN) and Long Short Term Memory models (LSTM) are used as building blocks. The performance of these models is evaluated using three publicly available datasets, the first dataset contains images for Lung cancer, second contains images for Covid-19 and third dataset contains images for Pneumonia and normal subjects. Combining three datasets creates a class imbalance problem which is resolved using pre-processing and data augmentation techniques. After data augmentation 1386 subjects are randomly chosen for each class. RESULTS It is observed that CNN when combined with LSTM (CNN-LSTM) produces significantly improved results (accuracy of 94.5 %) which is better than CNN and InceptionV3-LSTM. 3,5, and 10 fold cross validation is performed to verify all results calculated using three different classifiersConclusions:This research concludes that a single computer-aided diagnosis system can be developed for diagnosing multiple diseases.
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Affiliation(s)
- Aziz Ur Rehman
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Asma Naseer
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Saira Karim
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Maria Tamoor
- Forman Christian College University, Zahoor Ilahi Road, Lahore, Pakistan
| | - Samina Naz
- Muhammad Nawaz Sharif university of engineering and technology, Multan, Pakistan
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Mondal A, Shrivastava VK. A novel Parametric Flatten-p Mish activation function based deep CNN model for brain tumor classification. Comput Biol Med 2022; 150:106183. [PMID: 37859281 DOI: 10.1016/j.compbiomed.2022.106183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 08/22/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022]
Abstract
The brain tumor is one of the deadliest diseases of all cancers. Influenced by the recent developments of convolutional neural networks (CNNs) in medical imaging, we have formed a CNN based model called BMRI-Net for brain tumor classification. As the activation function is one of the important modules of CNN, we have proposed a novel parametric activation function named Parametric Flatten-p Mish (PFpM) to improve the performance. PFpM can tackle the significant disadvantages of the pre-existing activation functions like neuron death and bias shift effect. The parametric approach of PFpM also offers the model some extra flexibility to learn the complex patterns more accurately from the data. To validate our proposed methodology, we have used two brain tumor datasets namely Figshare and Br35H. We have compared the performance of our model with state-of-the-art deep CNN models like DenseNet201, InceptionV3, MobileNetV2, ResNet50 and VGG19. Further, the comparative performance of PFpM has been presented with various activation functions like ReLU, Leaky ReLU, GELU, Swish and Mish. We have performed record-wise and subject-wise (patient-level) experiments for Figshare dataset whereas only record-wise experiments have been performed in case of Br35H dataset due to unavailability of subject-wise information. Further, the model has been validated using hold-out and 5-fold cross-validation techniques. On Figshare dataset, our model has achieved 99.57% overall accuracy with hold-out validation and 98.45% overall accuracy with 5-fold cross validation in case of record-wise data split. On the other hand, the model has achieved 97.91% overall accuracy with hold-out validation and 97.26% overall accuracy with 5-fold cross validation in case of subject-wise data split. Similarly, for Br35H dataset, our model has attained 99% overall accuracy with hold-out validation and 98.33% overall accuracy with 5-fold cross validation using record-wise data split. Hence, our findings can introduce a secondary procedure in the clinical diagnosis of brain tumors.
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Affiliation(s)
- Ayan Mondal
- School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India.
| | - Vimal K Shrivastava
- School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India.
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Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7097044. [PMID: 35965780 PMCID: PMC9366270 DOI: 10.1155/2022/7097044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/15/2022] [Indexed: 11/17/2022]
Abstract
The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.
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A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9869948. [PMID: 35875749 PMCID: PMC9307347 DOI: 10.1155/2022/9869948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022]
Abstract
Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%.
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An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8491753. [PMID: 35855801 PMCID: PMC9288336 DOI: 10.1155/2022/8491753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 12/01/2022]
Abstract
Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection.
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An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7210928. [PMID: 35800696 PMCID: PMC9256343 DOI: 10.1155/2022/7210928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/17/2022] [Accepted: 05/27/2022] [Indexed: 11/17/2022]
Abstract
Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson's, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.
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Shakeel T, Habib S, Boulila W, Koubaa A, Javed AR, Rizwan M, Gadekallu TR, Sufiyan M. A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects. COMPLEX INTELL SYST 2022; 9:1027-1058. [PMID: 35668731 PMCID: PMC9151356 DOI: 10.1007/s40747-022-00767-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/15/2022] [Indexed: 12/23/2022]
Abstract
Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.
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Affiliation(s)
- Tanzeela Shakeel
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Shaista Habib
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Wadii Boulila
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Anis Koubaa
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Abdul Rehman Javed
- Department of Cyber Security, PAF Complex, E-9, Air University, Islamabad, Pakistan
| | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mahmood Sufiyan
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
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16
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Xiao Y, Yin H, Wang SH, Zhang YD. TReC: Transferred ResNet and CBAM for Detecting Brain Diseases. Front Neuroinform 2022; 15:781551. [PMID: 35002667 PMCID: PMC8733727 DOI: 10.3389/fninf.2021.781551] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/29/2021] [Indexed: 11/29/2022] Open
Abstract
Early diagnosis of pathological brains leads to early interventions in brain diseases, which may help control the illness conditions, prolong the life of patients, and even cure them. Therefore, the classification of brain diseases is a challenging but helpful task. However, it is hard to collect brain images, and the superabundance of images is also a great challenge for computing resources. This study proposes a new approach named TReC: Transferred Residual Networks (ResNet)-Convolutional Block Attention Module (CBAM), a specific model for small-scale samples, to detect brain diseases based on MRI. At first, the ResNet model, which is pre-trained on the ImageNet dataset, serves as initialization. Subsequently, a simple attention mechanism named CBAM is introduced and added into every ResNet residual block. At the same time, the fully connected (FC) layers of the ResNet are replaced with new FC layers, which meet the goal of classification. Finally, all the parameters of our model, such as the ResNet, the CBAM, and new FC layers, are retrained. The effectiveness of the proposed model is evaluated on brain magnetic resonance (MR) datasets for multi-class and two-class tasks. Compared with other state-of-the-art models, our model reaches the best performance for two-class and multi-class tasks on brain diseases.
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Affiliation(s)
- Yuteng Xiao
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom.,School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Hongsheng Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom
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Malik YS, Tamoor M, Naseer A, Wali A, Khan A. Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1169-1184. [PMID: 36093674 DOI: 10.3233/xst-221245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Medical image processing has gained much attention in developing computer-aided diagnosis (CAD) of diseases. CAD systems require deep understanding of X-rays, MRIs, CT scans and other medical images. The segmentation of the region of interest (ROI) from those images is one of the most crucial tasks. OBJECTIVE Although active contour model (ACM) is a popular method to segment ROIs in medical images, the final segmentation results highly depend on the initial placement of the contour. In order to overcome this challenge, the objective of this study is to investigate feasibility of developing a fully automated initialization process that can be optimally used in ACM to more effectively segment ROIs. METHODS In this study, a fully automated initialization algorithm namely, an adaptive Otsu-based initialization (AOI) method is proposed. Using this proposed method, an initial contour is produced and further refined by the ACM to produce accurate segmentation. For evaluation of the proposed algorithm, the ISIC-2017 Skin Lesion dataset is used due to its challenging complexities. RESULTS Four different supervised performance evaluation metrics are employed to measure the accuracy and robustness of the proposed algorithm. Using this AOI algorithm, the ACM significantly (p≤0.05) outperforms Otsu thresholding method with 0.88 Dice Score Coefficients (DSC) and 0.79 Jaccard Index (JI) and computational complexity of 0(mn). CONCLUSIONS After comparing proposed method with other state-of-the-art methods, our study demonstrates that the proposed methods is superior to other skin lesion segmentation methods, and it requires no training time, which also makes the new method more efficient than other deep learning and machine learning methods.
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Affiliation(s)
- Yushaa Shafqat Malik
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Science, Lahore, Pakistan
| | - Aamir Wali
- Department of Computer Science, National University of Computer and Emerging Science, Lahore, Pakistan
| | - Ayesha Khan
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
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Loraksa C, Mongkolsomlit S, Nimsuk N, Uscharapong M, Kiatisevi P. Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models. J Imaging 2021; 8:jimaging8010002. [PMID: 35049843 PMCID: PMC8779891 DOI: 10.3390/jimaging8010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 12/18/2022] Open
Abstract
Osteosarcoma is a rare bone cancer which is more common in children than in adults and has a high chance of metastasizing to the patient’s lungs. Due to initiated cases, it is difficult to diagnose and hard to detect the nodule in a lung at the early state. Convolutional Neural Networks (CNNs) are effectively applied for early state detection by considering CT-scanned images. Transferring patients from small hospitals to the cancer specialized hospital, Lerdsin Hospital, poses difficulties in information sharing because of the privacy and safety regulations. CD-ROM media was allowed for transferring patients’ data to Lerdsin Hospital. Digital Imaging and Communications in Medicine (DICOM) files cannot be stored on a CD-ROM. DICOM must be converted into other common image formats, such as BMP, JPG and PNG formats. Quality of images can affect the accuracy of the CNN models. In this research, the effect of different image formats is studied and experimented. Three popular medical CNN models, VGG-16, ResNet-50 and MobileNet-V2, are considered and used for osteosarcoma detection. The positive and negative class images are corrected from Lerdsin Hospital, and 80% of all images are used as a training dataset, while the rest are used to validate the trained models. Limited training images are simulated by reducing images in the training dataset. Each model is trained and validated by three different image formats, resulting in 54 testing cases. F1-Score and accuracy are calculated and compared for the models’ performance. VGG-16 is the most robust of all the formats. PNG format is the most preferred image format, followed by BMP and JPG formats, respectively.
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Affiliation(s)
- Chanunya Loraksa
- Medical Engineering, Faculty of Engineering, Thammasat University, Pathum Thani 12121, Thailand;
- Correspondence: ; Tel.: +66-(0)63-241-5888
| | | | - Nitikarn Nimsuk
- Medical Engineering, Faculty of Engineering, Thammasat University, Pathum Thani 12121, Thailand;
| | - Meenut Uscharapong
- Department of Medical Services, Lerdsin Hospital, Ministry of Public Health in Thailand, Bangkok 10500, Thailand; (M.U.); (P.K.)
| | - Piya Kiatisevi
- Department of Medical Services, Lerdsin Hospital, Ministry of Public Health in Thailand, Bangkok 10500, Thailand; (M.U.); (P.K.)
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