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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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Nan G, Liu Z, Du H, Zhu W, Xu S. Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:6214. [PMID: 37448061 DOI: 10.3390/s23136214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/26/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.
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Affiliation(s)
- Guojun Nan
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China
| | - Zhuo Liu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China
| | - Haibo Du
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China
| | - Wenwu Zhu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China
| | - Shuiqing Xu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China
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Saba T. Automated lung nodule detection and classification based on multiple classifiers voting. Microsc Res Tech 2019; 82:1601-1609. [DOI: 10.1002/jemt.23326] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 03/30/2019] [Accepted: 06/08/2019] [Indexed: 01/06/2023]
Affiliation(s)
- Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
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Iqbal S, Ghani Khan MU, Saba T, Mehmood Z, Javaid N, Rehman A, Abbasi R. Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microsc Res Tech 2019; 82:1302-1315. [DOI: 10.1002/jemt.23281] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 03/24/2019] [Accepted: 04/12/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Sajid Iqbal
- Department of Computer ScienceBahauddin Zakariya University Multan Pakistan
- Department of Computer Science and EngineeringUniversity of Engineering and Technology Lahore Pakistan
| | - Muhammad U. Ghani Khan
- Department of Computer Science and EngineeringUniversity of Engineering and Technology Lahore Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
| | - Zahid Mehmood
- Department of Computer EngineeringUniversity of Engineering and Technology Taxila Pakistan
| | - Nadeem Javaid
- Department of Computer ScienceCOMSATS University Islamabad Pakistan
| | - Amjad Rehman
- College of Computer and Information SystemsAl Yamamah University Riyadh Saudi Arabia
| | - Rashid Abbasi
- School of Computer and TechnologyAnhui University Hefei China
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Abbas N, Saba T, Rehman A, Mehmood Z, Javaid N, Tahir M, Khan NU, Ahmed KT, Shah R. Plasmodium
species aware based quantification of malaria parasitemia in light microscopy thin blood smear. Microsc Res Tech 2019; 82:1198-1214. [DOI: 10.1002/jemt.23269] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 02/19/2019] [Accepted: 03/15/2019] [Indexed: 01/03/2023]
Affiliation(s)
- Naveed Abbas
- Department of Computer ScienceIslamia College Peshawar KPK Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
| | - Amjad Rehman
- College of Business AdministrationAl Yamamah University Riyadh Saudi Arabia
| | - Zahid Mehmood
- Department of Computer EngineeringUniversity of Engineering and Technology Taxila Pakistan
| | - Nadeem Javaid
- Department of Computer ScienceCOMSATS University Islamabad Pakistan
| | - Muhammad Tahir
- Department of Computer ScienceCOMSATS University Islamabad, Attock Campus Pakistan
| | | | | | - Roaider Shah
- Department of Computer ScienceIslamia College Peshawar KPK Pakistan
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Younus ZS, Younus GT. Video Steganography Using Knight Tour Algorithm and LSB Method for Encrypted Data. JOURNAL OF INTELLIGENT SYSTEMS 2019. [DOI: 10.1515/jisys-2018-0225] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
This paper aims to propose a method for data hiding in video by utilizing the least significant bit (LSB) method and improving it by utilizing the knight tour algorithm for concealing the data inside the AVI video file and using a key function encryption method for encrypting the secret message. First, the secret message is encrypted by utilizing a mathematical equation. The key used in the equation is a set of random numbers. These numbers differ in each implementation to warrant the safety of the hidden message and to increase the security of the secret message. Then, the cover video was converted from a set of frames into separated images to take the advantage of the large size of video file. Afterward, the knight tour algorithm is utilized for random selecting of the pixels inside the frame utilized for embedding the secret message inside it to overcome the shortcoming of the conventional LSB method that utilized the serial selection of pixel and to increase the robustness and security of the proposed method. Afterward, the encrypted secret message is embedded inside the selected pixels by utilizing the LSB method in bits (7 and 8). The observational results have drawn that the proposed method has a superior performance compared to the previous steganography method in terms of quality by a high PSNR of 67.3638 dB and the lowest MSE of 0.2578. Furthermore, this method preserves the security where the secret message cannot be drawn out without knowing the decoding rules.
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Affiliation(s)
- Zeyad Safaa Younus
- Assistant Lecturer, Faculty of Computer Sciences and Mathematics, Software Engineering, University of Mosul, Mosul, Iraq, e-mail:
| | - Ghada Thanoon Younus
- Lecturer, Faculty of Computer Sciences and Mathematics, Computer Science, University of Mosul, Mosul, Iraq
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Microscopic malaria parasitemia diagnosis and grading on benchmark datasets. Microsc Res Tech 2018; 81:1042-1058. [DOI: 10.1002/jemt.23071] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 04/23/2018] [Accepted: 05/10/2018] [Indexed: 12/16/2022]
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Rehman A, Abbas N, Saba T, Mahmood T, Kolivand H. Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs. Microsc Res Tech 2018; 81:737-744. [DOI: 10.1002/jemt.23030] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 01/27/2018] [Accepted: 03/24/2018] [Indexed: 12/26/2022]
Affiliation(s)
- Amjad Rehman
- College of Computer and Information SystemsAl Yamamah UniversityRiyadh, 11512 Saudi Arabia
| | - Naveed Abbas
- Computer Science Department Islamia CollegeUniversity Peshawar Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan UniversityRiyadh, 11586 Saudi Arabia
| | - Toqeer Mahmood
- Department of Computer ScienceUniversity of Engineering and TechnologyTaxila, 47050 Pakistan
| | - Hoshang Kolivand
- Department of Computer ScienceLiverpool John Moores UniversityLiverpool, L3 3AF United Kingdom
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Iqbal S, Ghani MU, Saba T, Rehman A. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 2018; 81:419-427. [DOI: 10.1002/jemt.22994] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/14/2017] [Accepted: 01/03/2018] [Indexed: 11/12/2022]
Affiliation(s)
- Sajid Iqbal
- Department of Computer Science and Engineering; University of Engineering and Technology; Lahore Pakistan
- Department of Computer Science Bahauddin Zakariya University Multan Pakistan
| | - M. Usman Ghani
- Department of Computer Science and Engineering; University of Engineering and Technology; Lahore Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences; Prince Sultan University; Riyadh, 11586 Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems; Al Yamamah University; Riyadh, 11512 Saudi Arabia
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Jamal A, Hazim Alkawaz M, Rehman A, Saba T. Retinal imaging analysis based on vessel detection. Microsc Res Tech 2017; 80:799-811. [DOI: 10.1002/jemt.22867] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 12/28/2016] [Accepted: 02/27/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Arshad Jamal
- Faculty of Information Sciences & Engineering; Management and Science University; Shah Alam Selangor Malaysia
| | - Mohammed Hazim Alkawaz
- Faculty of Information Sciences & Engineering; Management and Science University; Shah Alam Selangor Malaysia
- Researcher, Center of Scientific Research and Development Nawroz University Kurdistan Region, Iraq
| | - Amjad Rehman
- College of Computer and Information Systems Al-Yamamah University Riyadh 11512; Saudi Arabia
| | - Tanzila Saba
- College of Computer and Information Sciences Prince Sultan University Riyadh 11586; Saudi Arabia
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Alkawaz MH, Sulong G, Saba T, Rehman A. Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2663-3] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Husham A, Hazim Alkawaz M, Saba T, Rehman A, Saleh Alghamdi J. Automated nuclei segmentation of malignant using level sets. Microsc Res Tech 2016; 79:993-997. [DOI: 10.1002/jemt.22733] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Revised: 06/24/2016] [Accepted: 07/09/2016] [Indexed: 11/09/2022]
Affiliation(s)
- Ahmed Husham
- Faculty of Computing; Universiti Teknologi; Johor Bahru Malaysia
| | - Mohammed Hazim Alkawaz
- Faculty of Information Sciences & Engineering; Management and Science University; Shah Alam Selangor Malaysia
- Faculty of Computer Sciences and Mathematics; University of Mosul; Mosul Iraq
| | - Tanzila Saba
- College of Computer and Information Sciences; Prince Sultan University; Riyadh 11586 Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems; Al-Yamamah University; Riyadh Saudi Arabia
| | - Jarallah Saleh Alghamdi
- College of Computer and Information Sciences; Prince Sultan University; Riyadh 11586 Saudi Arabia
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Abbas N, Saba T, Mohamad D, Rehman A, Almazyad AS, Al-Ghamdi JS. Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2474-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Rad AE, Rahim MSM, Rehman A, Saba T. Digital Dental X-ray Database for Caries Screening. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s13319-016-0096-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Basori AH, Alkawaz MH, Saba T, Rehman A. An overview of interactive wet cloth simulation in virtual reality and serious games. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1178600] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ahmad Hoirul Basori
- Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Mohammed Hazim Alkawaz
- Faculty of Information Sciences & Engineering, Management and Science University, Shah Alam, Malaysia
| | - Tanzila Saba
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems, Al-Yamamah University, Riyadh, Kingdom of Saudi Arabia
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Jadooki S, Mohamad D, Saba T, Almazyad AS, Rehman A. Fused features mining for depth-based hand gesture recognition to classify blind human communication. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2244-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Sabbaghi S, Aldeen M, Garnavi R, Varigos G, Doliantis C, Nicolopoulos J. Automated colour identification in melanocytic lesions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:3021-3024. [PMID: 26736928 DOI: 10.1109/embc.2015.7319028] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.
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The Correlation Between Blood Oxygenation Effects and Human Emotion Towards Facial Skin Colour of Virtual Human. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/s13319-015-0044-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wu J, Wang Y, Yu J, Shi X, Zhang J, Chen Y, Pang Y. Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2015; 32:248-257. [PMID: 26366596 DOI: 10.1364/josaa.32.000248] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency.
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Alkawaz MH, Mohamad D, Basori AH, Saba T. Blend Shape Interpolation and FACS for Realistic Avatar. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/s13319-015-0038-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Katuka JI, Mohamad D, Saba T, El-Affendi M, Mohamed AS. An Analysis of Object Appearance Information and Context Based Classification. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/s13319-014-0024-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Harouni M, Rahim MSM, Al-Rodhaan M, Saba T, Rehman A, Al-Dhelaan A. Online Persian/Arabic script classification without contextual information. THE IMAGING SCIENCE JOURNAL 2014. [DOI: 10.1179/1743131x14y.0000000083] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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