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Asiri AA, Shaf A, Ali T, Aamir M, Irfan M, Alqahtani S. Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability. PeerJ Comput Sci 2024; 10:e1878. [PMID: 38660148 PMCID: PMC11041936 DOI: 10.7717/peerj-cs.1878] [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: 11/06/2023] [Accepted: 01/24/2024] [Indexed: 04/26/2024]
Abstract
Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.
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Affiliation(s)
- Abdullah A. Asiri
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Najran, Saudi Arabia
| | - Ahmad Shaf
- Department of Computer Science, COMSATS University Islamabad, Sahiwal, Punjan, Pakistan
| | - Tariq Ali
- Department of Computer Science, COMSATS University Islamabad, Sahiwal, Punjan, Pakistan
| | - Muhammad Aamir
- Department of Computer Science, COMSATS University Islamabad, Sahiwal, Punjan, Pakistan
| | - Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University, Najran, Najran, Saudi Arabia
| | - Saeed Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Najran, Saudi Arabia
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks. Radiol Phys Technol 2021; 14:358-365. [PMID: 34338999 DOI: 10.1007/s12194-021-00633-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 07/24/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation is essential for the automatic evaluation of chronological changes in the brainstem and pituitary gland. Thus, the purpose of our study was to use deep learning to automatically segment internal organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) in midsagittal slices of 2D T1-weighted images. Deep learning for the automatic segmentation of seven regions in the images was accomplished using two different methods: patch-based segmentation and semantic segmentation. The networks used for patch-based segmentation were AlexNet, GoogLeNet, and ResNet50, whereas semantic segmentation was accomplished using SegNet, VGG16-weighted SegNet, and U-Net. The precision and Jaccard index were calculated, and the extraction accuracy of the six convolutional network (DCNN) systems was evaluated. The highest precision (0.974) was obtained with the VGG16-weighted SegNet, and the lowest precision (0.506) was obtained with ResNet50. Based on the data, calculation times, and Jaccard indices obtained in this study, segmentation on a 2D image may be considered a viable and effective approach. We found that the optimal automatic segmentation of organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) on brain sagittal T1-weighted images could be achieved using SegNet with VGG16.
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Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review. J Imaging 2021; 7:66. [PMID: 34460516 PMCID: PMC8321322 DOI: 10.3390/jimaging7040066] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
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Affiliation(s)
| | | | | | | | | | | | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland; (J.M.V.); (V.I.); (A.A.); (R.D.F.); (M.P.); (R.C.)
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Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021; 132:104337. [PMID: 33773193 DOI: 10.1016/j.compbiomed.2021.104337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/30/2022]
Abstract
In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors. A review of AI/ML research methods applied to MS found 216 papers using terms "Multiple Sclerosis", "machine learning", "artificial intelligence", "Bayes", and "Bayesian", of which 90 were relevant and recently published. More than half of these involve the detection and segmentation of MS lesions for quantitative analysis; however clinical and lifestyle risk factor assessment and prediction have largely been ignored. Of those that address risk factors, most provide only association studies for some factors and often fail to include the potential impact of confounding factors and bias (especially where these have causal explanations) that could affect data interpretation, such as reporting quality and medical care access in various countries. To address these gaps in the literature, we propose a causal Bayesian network approach to assessing risk factors for MS, which can address deficiencies in current epidemiological methods of producing risk measurements and makes better use of observational data.
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Affiliation(s)
- Morghan Hartmann
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
| | - Norman Fenton
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, E1 4NS, UK
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Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study. Int J Comput Assist Radiol Surg 2021; 16:529-542. [PMID: 33666859 DOI: 10.1007/s11548-021-02326-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/16/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images. METHODS PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics. RESULTS The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76-0.9994), 0.9132 (95% CI: 0.71-0.994) and 0.9164 (95% CI: 0.78-1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively. CONCLUSION Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics.
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Jin Q, Cui H, Sun C, Meng Z, Su R. Cascade knowledge diffusion network for skin lesion diagnosis and segmentation. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106881] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Magadza T, Viriri S. Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art. J Imaging 2021; 7:19. [PMID: 34460618 PMCID: PMC8321266 DOI: 10.3390/jimaging7020019] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 01/17/2023] Open
Abstract
Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.
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Affiliation(s)
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa;
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MRI brain tumor medical images analysis using deep learning techniques: a systematic review. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-020-00514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Debelee TG, Kebede SR, Schwenker F, Shewarega ZM. Deep Learning in Selected Cancers' Image Analysis-A Survey. J Imaging 2020; 6:121. [PMID: 34460565 PMCID: PMC8321208 DOI: 10.3390/jimaging6110121] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/19/2020] [Accepted: 10/26/2020] [Indexed: 02/08/2023] Open
Abstract
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
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Affiliation(s)
- Taye Girma Debelee
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, 120611 Addis Ababa, Ethiopia
| | - Samuel Rahimeto Kebede
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- Department of Electrical and Computer Engineering, Debreberhan University, 445 Debre Berhan, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, University of Ulm, 89081 Ulm, Germany;
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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