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Feng Z, Zheng Y, Wang P, Xue L, Yu M, Deng Z, Lei X, Chen G. Abnormal neonatal brain microstructure in gestational diabetes mellitus revealed by MRI texture analysis. Sci Rep 2023; 13:15720. [PMID: 37735200 PMCID: PMC10514262 DOI: 10.1038/s41598-023-43055-4] [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: 04/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
To investigate the value of MRI texture analysis in evaluating the effect of gestational diabetes mellitus (GDM) on neonatal brain microstructure development, we retrospectively collected images of neonates undergoing head MRI scans, including a GDM group (N1 = 37) and a healthy control group (N2 = 34). MaZda texture analysis software was used to extract the texture features from different sequence images and perform dimensionality reduction, and then the texture features selected by the lowest misjudgement rate method were imported into SPSS software for statistical analysis. In our study, we found that GDM affects the development of the microstructure of the neonatal brain, and different combinations of texture features have different recognition performances, such as different sequences and different brain regions. As a consequence, texture analysis combining multiple conventional MRI sequences has a high recognition performance in revealing the abnormal development of the brain microstructure of neonates born of mothers with GDM.
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Affiliation(s)
- Zhi Feng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Yurong Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Ping Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Liang Xue
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Mingling Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Zhitao Deng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Xiaoping Lei
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, Sichuan, China.
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.23 Tai Ping Street, Luzhou, 646000, Sichuan, China.
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Rajinikanth V, Vincent PMDR, Gnanaprakasam CN, Srinivasan K, Chang CY. Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features. Diagnostics (Basel) 2023; 13:diagnostics13111832. [PMID: 37296683 DOI: 10.3390/diagnostics13111832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/08/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.
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Affiliation(s)
- Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - P M Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - C N Gnanaprakasam
- Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, OMR, Chennai 600119, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
- Service Systems Technology Center, Industrial Technology Research Institute, Hsinchu 310401, Taiwan
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3
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Sun Y, Yang C, Xu Z, Lu Y. Recurrence Plot-Assisted Detection of Focal/Non-focal EEG Signals Using Ensemble Deep Features. J Med Biol Eng 2023. [DOI: 10.1007/s40846-023-00785-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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4
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Abdelaziz HA, Sallah M, Elgarayhi A, Al-Tahhan FE. Accurate automatic classification system for 3D CT images of some vertebrate remains from Egypt. JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2022. [DOI: 10.1080/16583655.2022.2096391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Hussien A. Abdelaziz
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohammed Sallah
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
- Higher Institute of Engineering and Technology, New Damietta, Egypt
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Fatma E. Al-Tahhan
- Mathematics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
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5
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Dubey AK, Mohbey KK. Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images. NEW GENERATION COMPUTING 2022; 41:61-84. [PMID: 36439302 PMCID: PMC9676871 DOI: 10.1007/s00354-022-00195-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.
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Affiliation(s)
- Ankit Kumar Dubey
- Department of Computer Science, Central University of Rajasthan, Ajmer, India
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Rai R, Das A, Ray S, Dhal KG. Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5313-5352. [PMID: 35694187 PMCID: PMC9171491 DOI: 10.1007/s11831-022-09766-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/07/2022] [Indexed: 05/27/2023]
Abstract
Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.
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Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Gangtok, Sikkim India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Swarnajit Ray
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
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7
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Jena JJ, Dash SCB, Satapathy SC. Stability analysis based parameter tuning of Social Group Optimization. COMPLEX INTELL SYST 2022; 8:3409-3435. [PMID: 35223377 PMCID: PMC8863571 DOI: 10.1007/s40747-022-00684-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 01/30/2022] [Indexed: 11/24/2022]
Abstract
Swarm-based optimization algorithms have been popularly used these days for optimization of various real world problems but sometimes it becomes hard to estimate the associated characteristics due to their stochastic nature. To ensure a steady performance of these techniques, it is essential to have knowledge about the range of variables, in which a particular algorithm always provides stable performance and performing stability analysis of an algorithm can help in providing some knowledge regarding the same. Many researchers have performed the stability analysis of several optimization algorithms and analyzed their behavior. Social Group Optimization (SGO) is a newly developed algorithm which has been proven to yield promising results when applied to many real world problems but in literature no work can be found on stability analysis of SGO. In this paper, Von Neumann stability analysis approach has been used for performing stability analysis of Social Group Optimization (SGO) to analyze the behavior of its algorithmic parameters and estimate the range in which they always give stable convergence. The results obtained have been supported by sufficient experimental analysis and simulated using eight benchmark function suite along with their shifted and rotated variations which prove that the algorithm performs better within the stable range and hence convergence is ensured.
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Dubey AK, Mohbey KK. Enabling CT-Scans for covid detection using transfer learning-based neural networks. J Biomol Struct Dyn 2022; 41:2528-2539. [PMID: 35129088 DOI: 10.1080/07391102.2022.2034668] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Today, we are coping with the pandemic, and the novel virus is covertly evolving day by day. Therefore, a precautionary system to deal with the issue is required as early as possible. The last few years were very challenging for doctors, vaccine makers, hospitals, and medical authorities to deal with the massive crowd to provide results for all patients and newcomers in the past months. Thus, these issues should be handled with a robust system that can accord with many people and deliver the results in a fraction of time without visiting public places and help reduce crowd gathering. So, to deal with these issues, we developed an AI model using transfer learning that can aid doctors and other people to get to know whether they were suffering from covid or not. In this paper, we have used VGG-19 (CNN-based) model with open-sourced COVID-CT (CTSI) dataset. The dataset consists of 349 images of COVID-19 of 216 patients and 463 images of NON-COVID-19. We have achieved an accuracy of 95%, precision of 96%, recall of 94%, and F1-Score of 96% from the experiments.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ankit Kumar Dubey
- Department of Computer Science, Central University of Rajasthan, Ajmer, India
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9
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Dey N, V. R. CNN-based segmentation of brain tumor from T2-weighted MRI slices. Magn Reson Imaging 2022. [DOI: 10.1016/b978-0-12-823401-3.00005-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Dey N, V. R. Magnetic resonance imaging: recording and reconstruction. Magn Reson Imaging 2022. [DOI: 10.1016/b978-0-12-823401-3.00003-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Khan MA, Rajinikanth V, Satapathy SC, Taniar D, Mohanty JR, Tariq U, Damaševičius R. VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics (Basel) 2021; 11:2208. [PMID: 34943443 PMCID: PMC8699868 DOI: 10.3390/diagnostics11122208] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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Affiliation(s)
| | - Venkatesan Rajinikanth
- Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu 600119, India;
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - David Taniar
- Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Jnyana Ranjan Mohanty
- School of Computer Applications, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Rajinikanth V, Sivakumar R, Hemanth DJ, Kadry S, Mohanty JR, Arunmozhi S, Raja NSM, Nhu NG. Automated classification of retinal images into AMD/non-AMD Class—a study using multi-threshold and Gassian-filter enhanced images. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00581-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00551-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Liaqat A, Khan MA, Sharif M, Mittal M, Saba T, Manic KS, Al Attar FNH. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Curr Med Imaging 2021; 16:1229-1242. [DOI: 10.2174/1573405616666200425220513] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/14/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022]
Abstract
Recent facts and figures published in various studies in the US show that approximately
27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that
the mortality rate is quite high in diagnosed cases. The early detection of these infections can save
precious human lives. As the manual process of these infections is time-consuming and expensive,
therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy
specialists in their clinics. Generally, an automated method of gastric infection detections
using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing,
feature extraction, segmentation of infected regions, and classification into their relevant
categories. These steps consist of various challenges that reduce the detection and recognition
accuracy as well as increase the computation time. In this review, authors have focused on the importance
of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and
the scope of endoscopy. Further, the general steps and highlighting the importance of each step
have been presented. A detailed discussion and future directions have been provided at the end.
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Affiliation(s)
- Amna Liaqat
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | | | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mamta Mittal
- Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, New Delhi, India
| | - Tanzila Saba
- Department of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - K. Suresh Manic
- Department of Electrical & Computer Engineering, National University of Science & Technology, Muscat, Oman
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Kadry S, Rajinikanth V, Raja NSM, Jude Hemanth D, Hannon NMS, Raj ANJ. Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: a study. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00539-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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17
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Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Cognit Comput 2020; 12:1011-1023. [PMID: 32837591 PMCID: PMC7429098 DOI: 10.1007/s12559-020-09751-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/29/2020] [Indexed: 12/26/2022]
Abstract
The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.
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Devi S, Sahoo MN, Bakshi S. A novel privacy-supporting 2-class classification technique for brain MRI images. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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19
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A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103429] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical level assessment of BT is normally performed using the bio-imaging technique, and MRI-assisted brain screening is one of the universal techniques. The proposed work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices. This work proposes the following DLAs to detect the BT: (i) implementing the pre-trained DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 with the deep-features-based SoftMax classifier; (ii) pre-trained DLAs with deep-features-based classification using decision tree (DT), k nearest neighbor (KNN), SVM-linear and SVM-RBF; and (iii) a customized VGG19 network with serially-fused deep-features and handcrafted-features to improve the BT detection accuracy. The experimental investigation was separately executed using Flair, T2 and T1C modality MRI slices, and a ten-fold cross validation was implemented to substantiate the performance of proposed DLA. The results of this work confirm that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair (>99%), T2 (>98%), T1C (>97%) and clinical images (>98%).
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An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.03.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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21
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Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, Shasky C, Zhang YD, Tavares JMR, Raja NSM. Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.013] [Citation(s) in RCA: 158] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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22
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Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.019] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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