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Benifa JVB, Chola C, Muaad AY, Hayat MAB, Bin Heyat MB, Mehrotra R, Akhtar F, Hussein HS, Vargas DLR, Castilla ÁK, Díez IDLT, Khan S. FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas. SENSORS (BASEL, SWITZERLAND) 2023; 23:6090. [PMID: 37447939 DOI: 10.3390/s23136090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.
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Affiliation(s)
- J V Bibal Benifa
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam 686635, India
| | - Channabasava Chola
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam 686635, India
| | - Abdullah Y Muaad
- Department of Studies in Computer Science, Mysore University, Manasagangothri, Mysore 570006, India
| | | | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Rajat Mehrotra
- Department of Examination and Analysis, Amity University, Noida 201303, India
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt
| | - Debora Libertad Ramírez Vargas
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres, 39011 Santander, Spain
- Department of Engineering and Projects, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Facultade de Engenharias, Universidade Internacional do Cuanza, Cuito EN250, Angola
| | - Ángel Kuc Castilla
- Department of Engineering and Projects, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- School of Engineering, Fundación Universitaria Internacional de Colombia, Bogotá 11001, Colombia
- Higher Polytechnic School, Universidad de La Romana, La Romana 22000, Dominican Republic
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications, University of Valladolid, 47011 Valladolid, Spain
| | - Salabat Khan
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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Alphonse AS, Benifa JVB, Muaad AY, Chola C, Heyat MBB, Murshed BAH, Abdel Samee N, Alabdulhafith M, Al-antari MA. A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images. Diagnostics (Basel) 2023; 13:diagnostics13061104. [PMID: 36980412 PMCID: PMC10047753 DOI: 10.3390/diagnostics13061104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.
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Affiliation(s)
- A. Sherly Alphonse
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - J. V. Bibal Benifa
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Channabasava Chola
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | | | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
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Chola C, Muaad AY, Bin Heyat MB, Benifa JVB, Naji WR, Hemachandran K, Mahmoud NF, Samee NA, Al-Antari MA, Kadah YM, Kim TS. BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification. Diagnostics (Basel) 2022; 12:diagnostics12112815. [PMID: 36428875 PMCID: PMC9689932 DOI: 10.3390/diagnostics12112815] [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: 10/13/2022] [Revised: 11/03/2022] [Accepted: 11/12/2022] [Indexed: 11/19/2022] Open
Abstract
Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.
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Affiliation(s)
- Channabasava Chola
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - J. V. Bibal Benifa
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kerala 686635, India
| | - Wadeea R. Naji
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - K. Hemachandran
- Department of Artificial Intelligence, Woxsen University, Hyderabad 502345, India
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Mugahed A. Al-Antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Yasser M. Kadah
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Biomedical Engineering Department, Cairo University, Giza 12613, Egypt
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Tae-Seong Kim
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
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