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Alyami J. Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions. EJNMMI REPORTS 2024; 8:7. [PMID: 38748374 PMCID: PMC10982256 DOI: 10.1186/s41824-024-00195-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/05/2024] [Indexed: 05/19/2024]
Abstract
Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.
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Affiliation(s)
- Jaber Alyami
- Department of Radiological Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- King Fahd Medical Research Center, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Smart Medical Imaging Research Group, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Medical Imaging and Artificial Intelligence Research Unit, Center of Modern Mathematical Sciences and its Applications, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
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Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM). COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7403302. [PMID: 36093488 PMCID: PMC9452941 DOI: 10.1155/2022/7403302] [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: 06/04/2022] [Revised: 07/02/2022] [Accepted: 07/29/2022] [Indexed: 01/07/2023]
Abstract
Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.
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Sajjad M, Ramzan F, Khan MUG, Rehman A, Kolivand M, Fati SM, Bahaj SA. Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation. Microsc Res Tech 2021; 84:3023-3034. [PMID: 34245203 DOI: 10.1002/jemt.23861] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 05/13/2021] [Accepted: 06/15/2021] [Indexed: 11/09/2022]
Abstract
With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
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Affiliation(s)
- Muhammad Sajjad
- National Center of Artificial Intelligence (NCAI), Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology (UET), Lahore, Pakistan
| | - Farheen Ramzan
- Department of Computer Science, University of Engineering and Technology (UET), Lahore, Pakistan
| | - Muhammad Usman Ghani Khan
- National Center of Artificial Intelligence (NCAI), Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology (UET), Lahore, Pakistan.,Department of Computer Science, University of Engineering and Technology (UET), Lahore, Pakistan
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics (AIDA) Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Mahyar Kolivand
- Department of Medicine, University of Liverpool, Liverpool, UK
| | - Suliman Mohamed Fati
- Artificial Intelligence & Data Analytics (AIDA) Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Saeed Ali Bahaj
- MIS Department College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech 2021. [PMID: 33522669 DOI: 10.1002/jemt] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Ibrahim Abunadi
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Amjad Rehman Khan
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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Khan AR, Doosti F, Karimi M, Harouni M, Tariq U, Fati SM, Ali Bahaj S. Authentication through gender classification from iris images using support vector machine. Microsc Res Tech 2021; 84:2666-2676. [PMID: 33991003 DOI: 10.1002/jemt.23816] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 04/03/2021] [Accepted: 04/24/2021] [Indexed: 11/07/2022]
Abstract
Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris-based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient-oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.
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Affiliation(s)
- Amjad Rehman Khan
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Fatemeh Doosti
- Department of Computer Engineering, Asharfi Isfahani University, Isfahan, Iran
| | - Mohsen Karimi
- Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran
| | - Majid Harouni
- Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Suliman Mohamed Fati
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Saeed Ali Bahaj
- MIS Department College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
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Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech 2021; 84:1462-1474. [PMID: 33522669 PMCID: PMC8014446 DOI: 10.1002/jemt.23702] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/27/2020] [Accepted: 12/27/2020] [Indexed: 12/13/2022]
Abstract
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Ibrahim Abunadi
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Amjad Rehman Khan
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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