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Alshehri S, Alahmari KA, Alasiry A. A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals. J Pers Med 2024; 14:354. [PMID: 38672981 PMCID: PMC11051468 DOI: 10.3390/jpm14040354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.
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Affiliation(s)
- Sarah Alshehri
- Otology and Neurotology, Department of Surgery, College of Medicine, King Khalid University, Abha 61423, Saudi Arabia
| | - Khalid A. Alahmari
- Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61423, Saudi Arabia;
| | - Areej Alasiry
- Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha 61423, Saudi Arabia;
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2
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Awais M, Abdal MN, Akram T, Alasiry A, Marzougui M, Masood A. An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization. Front Oncol 2024; 14:1328200. [PMID: 38505591 PMCID: PMC10949894 DOI: 10.3389/fonc.2024.1328200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/25/2024] [Indexed: 03/21/2024] Open
Abstract
In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.
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Affiliation(s)
- Muhammad Awais
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
- Department of Computer Engineering, TED University, Ankara, Türkiye
| | - Md. Nazmul Abdal
- Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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Bibi S, Khan MA, Shah JH, Damaševičius R, Alasiry A, Marzougui M, Alhaisoni M, Masood A. MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection. Diagnostics (Basel) 2023; 13:3063. [PMID: 37835807 PMCID: PMC10572512 DOI: 10.3390/diagnostics13193063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/19/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.
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Affiliation(s)
- Sobia Bibi
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan; (S.B.); (J.H.S.)
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102-2801, Lebanon;
- Department of CS, HITEC University, Taxila 47080, Pakistan
| | - Jamal Hussain Shah
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan; (S.B.); (J.H.S.)
| | - Robertas Damaševičius
- Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia;
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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Khan IR, Sangari MS, Shukla PK, Aleryani A, Alqahtani O, Alasiry A, Alouane MTH. An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification. Biomimetics (Basel) 2023; 8:438. [PMID: 37754189 PMCID: PMC10527543 DOI: 10.3390/biomimetics8050438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/01/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset's mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA's performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.
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Affiliation(s)
| | - M. Siva Sangari
- Department of CSE, KPR Institute of Engineering and Technology, Coimbatore 641407, India;
| | - Piyush Kumar Shukla
- Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal 462033, India
| | - Aliya Aleryani
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia; (O.A.); (A.A.); (M.T.-H.A.)
| | - Omar Alqahtani
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia; (O.A.); (A.A.); (M.T.-H.A.)
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia; (O.A.); (A.A.); (M.T.-H.A.)
| | - M. Turki-Hadj Alouane
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia; (O.A.); (A.A.); (M.T.-H.A.)
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Hussain M, Khan MA, Damaševičius R, Alasiry A, Marzougui M, Alhaisoni M, Masood A. SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm. Diagnostics (Basel) 2023; 13:2869. [PMID: 37761236 PMCID: PMC10527569 DOI: 10.3390/diagnostics13182869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top-bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.
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Affiliation(s)
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 13-5053, Lebanon
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | - Robertas Damaševičius
- Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia;
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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Srinivasan S, Gunasekaran S, Mathivanan SK, Jayagopal P, Khan MA, Alasiry A, Marzougui M, Masood A. A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA. Diagnostics (Basel) 2023; 13:diagnostics13081385. [PMID: 37189485 DOI: 10.3390/diagnostics13081385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior-posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.
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Affiliation(s)
- Saravanan Srinivasan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
| | - Subathra Gunasekaran
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India
| | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Prabhu Jayagopal
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | | | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
- Electronics and Micro-Electronics Laboratory, Faculty of Sciences, University of Monastir, Monastir 5000, Tunisia
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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Malik S, Akram T, Awais M, Khan MA, Hadjouni M, Elmannai H, Alasiry A, Marzougui M, Tariq U. An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics. Diagnostics (Basel) 2023; 13:diagnostics13071285. [PMID: 37046503 PMCID: PMC10093272 DOI: 10.3390/diagnostics13071285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 03/30/2023] Open
Abstract
The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results.
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Menon SP, Shukla PK, Sethi P, Alasiry A, Marzougui M, Alouane MTH, Khan AA. An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications. Sensors (Basel) 2023; 23:3004. [PMID: 36991714 PMCID: PMC10052330 DOI: 10.3390/s23063004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/19/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. MAIN PROBLEM Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. METHODOLOGY This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). RESULTS Compared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.
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Affiliation(s)
- Sindhu P. Menon
- School of Computing and Information Technology, Reva University, Bangalore 560064, Karnataka, India
| | - Prashant Kumar Shukla
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, Andhra Pradesh, India
| | - Priyanka Sethi
- Department of Physiotherapy, Faculty of Allied Health Sciences, Manav Rachna International Institute of Research & Studies, Faridabad 121004, Haryana, India
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | | | - Arfat Ahmad Khan
- Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand
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Hemachandran K, Alasiry A, Marzougui M, Ganie SM, Pise AA, Alouane MTH, Chola C. Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease. Diagnostics (Basel) 2023; 13:diagnostics13030534. [PMID: 36766640 PMCID: PMC9914762 DOI: 10.3390/diagnostics13030534] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/07/2023] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models.
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Affiliation(s)
- K. Hemachandran
- Department of Analytics, School of Business, Woxsen University, Hyderabad 502345, Telangana, India
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
| | - Shahid Mohammad Ganie
- Department of Analytics, School of Business, Woxsen University, Hyderabad 502345, Telangana, India
| | - Anil Audumbar Pise
- Siatik Premier Google Cloud Platform Partner, Johannesburg 2000, South Africa
- School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2000, South Africa
- School Saveetha School of Engineering, Chennai 600124, Tamil Nadu, India
| | - M. Turki-Hadj Alouane
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
- Correspondence:
| | - Channabasava Chola
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, Karnataka, India
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