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Amin J, Shazadi I, Sharif M, Yasmin M, Almujally NA, Nam Y. Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning. Heliyon 2024; 10:e30954. [PMID: 38779022 PMCID: PMC11109848 DOI: 10.1016/j.heliyon.2024.e30954] [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: 01/08/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Irum Shazadi
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Yunyoung Nam
- Department of ICT Convergence, Soonchunhyang University, Asan, 31538, South Korea
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2
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Venkaiahppalaswamy B, Prasad Reddy PVGD, Batha S. Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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3
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Umer MJ, Sharif MI. A Comprehensive Survey on Quantum Machine Learning and Possible Applications. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2022. [DOI: 10.4018/ijehmc.315730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
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4
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Ramzan M, Raza M, Sharif MI, Kadry S. Gastrointestinal Tract Polyp Anomaly Segmentation on Colonoscopy Images Using Graft-U-Net. J Pers Med 2022; 12:jpm12091459. [PMID: 36143244 PMCID: PMC9503374 DOI: 10.3390/jpm12091459] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/28/2022] [Accepted: 09/01/2022] [Indexed: 11/21/2022] Open
Abstract
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. So, the early examination of polyps can decrease the chance of the polyps growing into cancer, such as adenomas, which can change into cancer. Deep learning-based diagnostic systems play a vital role in diagnosing diseases in the early stages. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages. The preprocessing technique is used to improve the contrast of the colonoscopy frames. Graft-U-Net comprises encoder and decoder blocks where the encoder analyzes features, while the decoder performs the features’ synthesizing processes. The Graft-U-Net model offers better segmentation results than existing deep learning models. The experiments were conducted using two open-access datasets, Kvasir-SEG and CVC-ClinicDB. The datasets were prepared from the large bowel of the gastrointestinal tract by performing a colonoscopy procedure. The anticipated model outperforms in terms of its mean Dice of 96.61% and mean Intersection over Union (mIoU) of 82.45% with the Kvasir-SEG dataset. Similarly, with the CVC-ClinicDB dataset, the method achieved a mean Dice of 89.95% and an mIoU of 81.38%.
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Affiliation(s)
- Muhammad Ramzan
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
- Correspondence:
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 999095, Lebanon
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5
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Shaukat N, Amin J, Sharif M, Azam F, Kadry S, Krishnamoorthy S. Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. J Pers Med 2022; 12:jpm12091454. [PMID: 36143239 PMCID: PMC9501488 DOI: 10.3390/jpm12091454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022] Open
Abstract
Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.
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Affiliation(s)
- Natasha Shaukat
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
| | - Javeria Amin
- Department of Computer Science, University of Wah, Wah Campus, Wah Cantt 47010, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
- Correspondence: (M.S.); (S.K.)
| | - Faisal Azam
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
| | - Sujatha Krishnamoorthy
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China
- Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China
- Correspondence: (M.S.); (S.K.)
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6
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Amin J, Anjum MA, Malik M. Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108881] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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7
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Yunus U, Amin J, Sharif M, Yasmin M, Kadry S, Krishnamoorthy S. Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network. LIFE (BASEL, SWITZERLAND) 2022; 12:life12081126. [PMID: 36013305 PMCID: PMC9410095 DOI: 10.3390/life12081126] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/23/2022]
Abstract
Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.
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Affiliation(s)
- Usman Yunus
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47010, Pakistan;
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
| | - Sujatha Krishnamoorthy
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China
- Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China
- Correspondence:
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8
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Amin J, Anjum MA, Sharif M, Kadry S, Nadeem A, Ahmad SF. Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12040823. [PMID: 35453870 PMCID: PMC9025116 DOI: 10.3390/diagnostics12040823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/17/2022] Open
Abstract
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan;
| | | | - Muhammad Sharif
- Department of Computer Science, Comsats University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan;
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4609 Kristiansand, Norway
- Correspondence:
| | - Ahmed Nadeem
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
| | - Sheikh F. Ahmad
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
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9
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Gupta S, Thakur S, Gupta A. Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:14475-14501. [PMID: 35233182 PMCID: PMC8876080 DOI: 10.1007/s11042-022-12103-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/14/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Diabetic Retinopathy (DR) is defined as the Diabetes Mellitus difficulty that harms the blood vessels in the retina. It is also known as a silent disease and cause mild vision issues or no symptoms. In order to enhance the chances of effective treatment, yearly eye tests are vital for premature discovery. Hence, it uses fundus cameras for capturing retinal images, but due to its size and cost, it is a troublesome for extensive screening. Therefore, the smartphones are utilized for scheming low-power, small-sized, and reasonable retinal imaging schemes to activate automated DR detection and DR screening. In this article, the new DIY (do it yourself) smartphone enabled camera is used for smartphone based DR detection. Initially, the preprocessing like green channel transformation and CLAHE (Contrast Limited Adaptive Histogram Equalization) are performed. Further, the segmentation process starts with optic disc segmentation by WT (watershed transform) and abnormality segmentation (Exudates, microaneurysms, haemorrhages, and IRMA) by Triplet half band filter bank (THFB). Then the different features are extracted by Haralick and ADTCWT (Anisotropic Dual Tree Complex Wavelet Transform) methods. Using life choice-based optimizer (LCBO) algorithm, the optimal features are chosen from the mined features. Then the selected features are applied to the optimized hybrid ML (machine learning) classifier with the combination of NN and DCNN (Deep Convolutional Neural Network) in which the SSD (Social Ski-Driver) is utilized for the best weight values of hybrid classifier to categorize the severity level as mild DR, severe DR, normal, moderate DR, and Proliferative DR. The proposed work is simulated in python environment and to test the efficiency of the proposed scheme the datasets like APTOS-2019-Blindness-Detection, and EyePacs are used. The model has been evaluated using different performance metrics. The simulation results verified that the suggested scheme is provides well accuracy for each dataset than other current approaches.
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Affiliation(s)
- Shubhi Gupta
- Department of Computer Science, Amity University, Uttar Pradesh, India
| | | | - Ashutosh Gupta
- U.P. Rajarshi Tandon Open University, Uttar Pradesh, India
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10
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Deep feed forward neural network-based screening system for diabetic retinopathy severity classification using the lion optimization algorithm. Graefes Arch Clin Exp Ophthalmol 2021; 260:1245-1263. [PMID: 34505925 DOI: 10.1007/s00417-021-05375-x] [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: 04/21/2021] [Revised: 07/19/2021] [Accepted: 08/06/2021] [Indexed: 10/20/2022] Open
Abstract
Diabetic Retinopathy (DR) has become a major cause of blindness in recent years. Diabetic patients should be screened on a regular basis for early detection, which can help them avoid blindness. Furthermore, the number of diabetic patients undergoing these screening procedures is rapidly increasing, resulting in increased workload for ophthalmologists. An efficient screening system that assists ophthalmologists in DR diagnosis saves ophthalmologists a lot of time and effort. To address this issue, an automatic DR detection screening system is required to improve diagnosis speed and detection accuracy. Appropriate treatment can be provided to patients to prevent vision loss if the severity levels of DR are accurately diagnosed in the early stages. A growing number of screening systems for DR diagnosis have been developed in recent years using various deep learning models, and the majority of the published work did not include any optimization algorithm in the neural network for severity classification. The use of an optimization algorithm with the necessary hyper parameter tuning will improve the model's performance. Considering this as motivation, we proposed a five-phase DFNN-LOA model. The DFNN-LOA algorithm presented here has five phases: (i) pre-processing, (ii) optic disc detection, (iii) segmentation, (iv) feature extraction, and (v) severity classification. The proposed model's experimental analysis is carried out on the MESSIDOR dataset. The experimental results show that the proposed DFNN-LOA model has superior characteristics, with maximum accuracy, sensitivity, specificity, F1-score, PPV, and NPV of 97.6%, 98.4%, 90.7%, 96.5%, 94.6%, and 97.1%, respectively.
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11
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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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12
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A review of diabetic retinopathy: Datasets, approaches, evaluation metrics and future trends. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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13
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Rehman A. Light microscopic iris classification using ensemble multi-class support vector machine. Microsc Res Tech 2021; 84:982-991. [PMID: 33438285 DOI: 10.1002/jemt.23659] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 10/24/2020] [Accepted: 11/06/2020] [Indexed: 02/04/2023]
Abstract
Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law-enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag-of-Words model to extract distinct and robust features from the iris image, followed by ensemble multi-class-SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi-class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA-v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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14
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Sadad T, Rehman A, Munir A, Saba T, Tariq U, Ayesha N, Abbasi R. Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc Res Tech 2021; 84:1296-1308. [PMID: 33400339 DOI: 10.1002/jemt.23688] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 10/14/2020] [Accepted: 12/06/2020] [Indexed: 11/11/2022]
Abstract
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.
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Affiliation(s)
- Tariq Sadad
- Department of Computer Science, University of Central Punjab, Lahore, Pakistan
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Asim Munir
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Tanzila Saba
- Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Usman Tariq
- College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Noor Ayesha
- School of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Rashid Abbasi
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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15
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Saba T. Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features. Microsc Res Tech 2021; 84:1272-1283. [PMID: 33399251 DOI: 10.1002/jemt.23686] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/15/2020] [Accepted: 11/30/2020] [Indexed: 12/31/2022]
Abstract
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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16
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Narhari BB, Murlidhar BK, Sayyad AD, Sable GS. Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier. BIO-ALGORITHMS AND MED-SYSTEMS 2020. [DOI: 10.1515/bams-2020-0053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Abstract
Objectives
The focus of this paper is to introduce an automated early Diabetic Retinopathy (DR) detection scheme from colour fundus images through enhanced segmentation and classification strategies by analyzing blood vessels.
Methods
The occurrence of DR is increasing from the past years, impacting the eyes due to a sudden rise in the glucose level of blood. All over the world, half of the people who are under age 70 are severely suffered from diabetes. The patients who are affected by DR will lose their vision during the absence of early recognition of DR and appropriate treatment. To decrease the growth and occurrence of loss of vision, the early detection and timely treatment of DR are desirable. At present, deep learning models have presented better performance using retinal images for DR detection. In this work, the input retinal fundus images are initially subjected to pre-processing that undergoes contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Further, the optimized binary thresholding-based segmentation is done for blood vessel segmentation. For the segmented image, Tri-level Discrete Level Decomposition (Tri-DWT) is performed to decompose it. In the feature extraction phase, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrices (GLCMs) are extracted. Next, the classification of images is done through the combination of two algorithms, one is Neural Network (NN), and the other Convolutional Neural Network (CNN). The extracted features are subjected to NN, and the tri-DWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Rate-based Crow Search Algorithm (FR-CSA), in which few parameters are optimized for attaining maximum detection accuracy.
Results
The proposed DR detection model was implemented in MATLAB 2018a, and the analysis was done using three datasets, HRF, Messidor, and DIARETDB.
Conclusions
The developed FR-CSA algorithm has the best detection accuracy in diagnosing DR.
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Affiliation(s)
- Bansode Balbhim Narhari
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
| | - Bakwad Kamlakar Murlidhar
- Department of Electronics Engineering , Puranmal Lahoti Govt. Polytechnic College, MSBTE, Latur , Mumbai , India
| | - Ajij Dildar Sayyad
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
| | - Ganesh Shahubha Sable
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
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Sharif MI, Li JP, Naz J, Rashid I. A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.12.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA. Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04650-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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