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Noman MK, Shamsul Islam SM, Jafar Jalali SM, Abu-Khalaf J, Lavery P. BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detection. PLoS One 2024; 19:e0281568. [PMID: 38917071 PMCID: PMC11198790 DOI: 10.1371/journal.pone.0281568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 11/05/2023] [Indexed: 06/27/2024] Open
Abstract
Deep learning, a subset of machine learning that utilizes neural networks, has seen significant advancements in recent years. These advancements have led to breakthroughs in a wide range of fields, from natural language processing to computer vision, and have the potential to revolutionize many industries or organizations. They have also demonstrated exceptional performance in the identification and mapping of seagrass images. However, these deep learning models, particularly the popular Convolutional Neural Networks (CNNs) require architectural engineering and hyperparameter tuning. This paper proposes a Deep Neuroevolutionary (DNE) model that can automate the architectural engineering and hyperparameter tuning of CNNs models by developing and using a novel metaheuristic algorithm, named 'Boosted Atomic Orbital Search (BAOS)'. The proposed BAOS is an improved version of the recently proposed Atomic Orbital Search (AOS) algorithm which is based on the principle of atomic model and quantum mechanics. The proposed algorithm leverages the power of the Lévy flight technique to boost the performance of the AOS algorithm. The proposed DNE algorithm (BAOS-CNN) is trained, evaluated and compared with six popular optimisation algorithms on a patch-based multi-species seagrass dataset. This proposed BAOS-CNN model achieves the highest overall accuracy (97.48%) among the seven evolutionary-based CNN models. The proposed model also achieves the state-of-the-art overall accuracy of 92.30% and 93.5% on the publicly available four classes and five classes version of the 'DeepSeagrass' dataset, respectively. This multi-species seagrass dataset is available at: https://ro.ecu.edu.au/datasets/141/.
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Affiliation(s)
- Md Kislu Noman
- School of Science, Edith Cowan University, Perth, Australia
| | | | | | | | - Paul Lavery
- School of Science, Edith Cowan University, Perth, Australia
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Pang T, Wong JHD, Ng WL, Chan CS, Wang C, Zhou X, Yu Y. Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification. Phys Med Biol 2024; 69:065006. [PMID: 38373345 DOI: 10.1088/1361-6560/ad2a95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/19/2024] [Indexed: 02/21/2024]
Abstract
Objective.Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning radiomics (DLR).Approach.In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of DLR in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks to extract visual features as radiomics for multi-category classification based on breast imaging reporting and data system. Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity.Main results.To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of DLR; and, (ii) improve the readability of generated medical reports.Significance.Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.
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Affiliation(s)
- Ting Pang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453000, People's Republic of China
- Center of Image and Signal Processing, Faculty of Computer Science and Infomation Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, 453000, People's Republic of China
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Wei Lin Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Chee Seng Chan
- Center of Image and Signal Processing, Faculty of Computer Science and Infomation Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
| | - Chang Wang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453000, People's Republic of China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, 453000, People's Republic of China
| | - Xuezhi Zhou
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453000, People's Republic of China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, 453000, People's Republic of China
| | - Yi Yu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453000, People's Republic of China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, 453000, People's Republic of China
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Kumar Lilhore U, Simaiya S, Sharma YK, Kaswan KS, Rao KBVB, Rao VVRM, Baliyan A, Bijalwan A, Alroobaea R. A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization. Sci Rep 2024; 14:4299. [PMID: 38383520 PMCID: PMC10881962 DOI: 10.1038/s41598-024-54212-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.
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Affiliation(s)
- Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, 140413, India
| | - Sarita Simaiya
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, 140413, India
| | - Yogesh Kumar Sharma
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur, AP, India
| | - Kuldeep Singh Kaswan
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
| | - K B V Brahma Rao
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur, AP, India
| | - V V R Maheswara Rao
- Departmentt of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, India
| | - Anupam Baliyan
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, 140413, India
| | | | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
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Monica KM, Shreeharsha J, Falkowski-Gilski P, Falkowska-Gilska B, Awasthy M, Phadke R. Melanoma skin cancer detection using mask-RCNN with modified GRU model. Front Physiol 2024; 14:1324042. [PMID: 38292449 PMCID: PMC10825805 DOI: 10.3389/fphys.2023.1324042] [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: 10/24/2023] [Accepted: 12/18/2023] [Indexed: 02/01/2024] Open
Abstract
Introduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic images are acquired from two online benchmark datasets: International Skin Imaging Collaboration (ISIC) 2020 and Human against Machine (HAM) 10000. Subsequently, a normalization technique is employed on the dermoscopic images to decrease noise impact, outliers, and variations in the pixels. Furthermore, cancerous regions in the pre-processed images are segmented utilizing the mask-faster Region based Convolutional Neural Network (RCNN) model. The mask-RCNN model offers precise pixellevel segmentation by accurately delineating object boundaries. From the partitioned cancerous regions, discriminative feature vectors are extracted by applying three pre-trained CNN models, namely ResNeXt101, Xception, and InceptionV3. These feature vectors are passed into the modified Gated Recurrent Unit (GRU) model for MSC classification. In the modified GRU model, a swish-Rectified Linear Unit (ReLU) activation function is incorporated that efficiently stabilizes the learning process with better convergence rate during training. Results and discussion: The empirical investigation demonstrate that the modified GRU model attained an accuracy of 99.95% and 99.98% on the ISIC 2020 and HAM 10000 datasets, where the obtained results surpass the conventional detection models.
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Affiliation(s)
- K. M. Monica
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - J. Shreeharsha
- Department of Computer Science and Engineering, Rao Bahadur Y. Mahabaleswarappa Engineering College, Ballari, Karnataka, India
| | | | | | - Mohan Awasthy
- Department of Engineering and Technology, Bharati Vidyapeeth Peeth Deemed to be University, Navi Mumbai, Maharashtra, India
| | - Rekha Phadke
- Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India
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Josphineleela R, Raja Rao PBV, Shaikh A, Sudhakar K. A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization. J Digit Imaging 2023; 36:2210-2226. [PMID: 37322306 PMCID: PMC10502001 DOI: 10.1007/s10278-023-00848-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/15/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classification. Convolutional neural networks (CNNs) can classify melanoma skin cancer images. But, it suffers from an overfitting problem. Therefore, to overcome this problem and to classify both benign and malignant tumors efficiently, the multi-stage faster RCNN-based iSPLInception (MFRCNN-iSPLI) method is proposed. Then, the test dataset is used for evaluating the proposed model performance. The faster RCNN is employed directly to perform image classification. This may heavily raise computation time and network complications. So, the iSPLInception model is applied in the multi-stage classification. In this, the iSPLInception model is formulated using the Inception-ResNet design. For candidate box deletion, the prairie dog optimization algorithm is utilized. We have utilized two skin disease datasets, namely, ISIC 2019 Skin lesion image classification and the HAM10000 dataset for conducting experimental results. The methods' accuracy, precision, recall, and F1 score values are calculated, and the results are compared with the existing methods such as CNN, hybrid DL, Inception v3, and VGG19. With 95.82% accuracy, 96.85% precision, 96.52% recall, and 0.95% F1 score values, the output analysis of each measure verified the prediction and classification effectiveness of the method.
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Affiliation(s)
- R Josphineleela
- Department of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India.
| | - P B V Raja Rao
- Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), JNTUK, Bhimavaram, Kakinada, Andhra Pradesh, India
| | - Amir Shaikh
- Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - K Sudhakar
- Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
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Huang C, Li W, Ren X, Tang M, Zhang K, Zhuo F, Dou X, Yu B. The Crucial Roles and Research Advances of cGAS-STING Pathway in Cutaneous Disorders. Inflammation 2023:10.1007/s10753-023-01812-7. [PMID: 37083899 PMCID: PMC10119538 DOI: 10.1007/s10753-023-01812-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/14/2023] [Accepted: 03/29/2023] [Indexed: 04/22/2023]
Abstract
The cGAS-STING signaling pathway senses the presence of cytosolic DNA, induces strong type I interferon responses, and enhances inflammatory cytokine production, placing it as an important axis in infection, autoimmunity, and tumor immunity. Recent studies have shown that the abnormalities and/or dysfunctions of cGAS-STING signaling are closely related to the pathogenesis of skin diseases and/or cancers. Additionally, a variety of new therapeutics targeting the cGAS-STING signaling are in development for the treatment of skin disorders. However, the precise molecular mechanisms of cGAS-STING-mediated cutaneous disorders have not been fully elucidated. In this review, we will summarize the regulatory roles and mechanisms of cGAS-STING signaling in skin disorders and recent progresses of cGAS-STING-related drugs as well as their potential clinical applications.
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Affiliation(s)
- Cong Huang
- Department of Dermatology, Skin Research Institute of Peking University Shenzhen Hospital, Peking University Shenzhen Hospital, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Wenting Li
- Department of Dermatology, Skin Research Institute of Peking University Shenzhen Hospital, Peking University Shenzhen Hospital, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Xuanyao Ren
- Biomedical Research Institute, Shenzhen Peking University - the Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Mindan Tang
- Biomedical Research Institute, Shenzhen Peking University - the Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Kaoyuan Zhang
- Biomedical Research Institute, Shenzhen Peking University - the Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Fan Zhuo
- Department of Dermatology, Skin Research Institute of Peking University Shenzhen Hospital, Peking University Shenzhen Hospital, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Xia Dou
- Department of Dermatology, Skin Research Institute of Peking University Shenzhen Hospital, Peking University Shenzhen Hospital, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Bo Yu
- Department of Dermatology, Skin Research Institute of Peking University Shenzhen Hospital, Peking University Shenzhen Hospital, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China.
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Obayya M, Alhebri A, Maashi M, S. Salama A, Mustafa Hilal A, Alsaid MI, Osman AE, Alneil AA. Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer. Cancers (Basel) 2023; 15:cancers15072146. [PMID: 37046806 PMCID: PMC10093373 DOI: 10.3390/cancers15072146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023] Open
Abstract
Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.
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Affiliation(s)
- Marwa Obayya
- Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Adeeb Alhebri
- Department of Accounting, Applied College, King Khalid University, Mohail Asser 63311, Saudi Arabia
| | - Mashael Maashi
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Ahmed S. Salama
- Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
| | - Anwer Mustafa Hilal
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Mohamed Ibrahim Alsaid
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Azza Elneil Osman
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Amani A. Alneil
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
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AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions. Cancers (Basel) 2023; 15:cancers15041183. [PMID: 36831525 PMCID: PMC9953963 DOI: 10.3390/cancers15041183] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/15/2023] Open
Abstract
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
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9
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TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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10
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Jalali SMJ, Ahmadian M, Ahmadian S, Hedjam R, Khosravi A, Nahavandi S. X-ray image based COVID-19 detection using evolutionary deep learning approach. EXPERT SYSTEMS WITH APPLICATIONS 2022; 201:116942. [PMID: 35378906 PMCID: PMC8966159 DOI: 10.1016/j.eswa.2022.116942] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/24/2021] [Accepted: 03/17/2022] [Indexed: 06/01/2023]
Abstract
Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.
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Affiliation(s)
| | - Milad Ahmadian
- Department of Computer Engineering, Razi University, Kermanshah, Iran
| | - Sajad Ahmadian
- Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | - Rachid Hedjam
- Department of Computer science, Sultan Qaboos University, Muscat, Sultanate of Oman
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
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11
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Lee JRH, Pavlova M, Famouri M, Wong A. Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images. BMC Med Imaging 2022; 22:143. [PMID: 35945505 PMCID: PMC9364616 DOI: 10.1186/s12880-022-00871-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/26/2022] [Indexed: 11/25/2022] Open
Abstract
Background Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design. Results We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts. Conclusion The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
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Affiliation(s)
- James Ren Hou Lee
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, Canada.
| | - Maya Pavlova
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, Canada.,DarwinAI Corp, Waterloo, Canada
| | | | - Alexander Wong
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, Canada.,Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, Canada.,DarwinAI Corp, Waterloo, Canada
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12
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A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs. SENSORS 2022; 22:s22155566. [PMID: 35898070 PMCID: PMC9332569 DOI: 10.3390/s22155566] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/10/2022] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating diverse deep neural networks with attention mechanisms for undertaking lung abnormality and COVID-19 diagnosis using respiratory, speech, and coughing audio inputs. Specifically, four base deep networks are proposed, which include attention-based Convolutional Recurrent Neural Network (A-CRNN), attention-based bidirectional Long Short-Term Memory (A-BiLSTM), attention-based bidirectional Gated Recurrent Unit (A-BiGRU), as well as Convolutional Neural Network (CNN). A Particle Swarm Optimization (PSO) algorithm is used to optimize the training parameters of each network. An ensemble mechanism is used to integrate the outputs of these base networks by averaging the probability predictions of each class. Evaluated using respiratory ICBHI, Coswara breathing, speech, and cough datasets, as well as a combination of ICBHI and Coswara breathing databases, our ensemble model and base networks achieve ICBHI scores ranging from 0.920 to 0.9766. Most importantly, the empirical results indicate that a positive COVID-19 diagnosis can be distinguished to a high degree from other more common respiratory diseases using audio recordings, based on the combined ICBHI and Coswara breathing datasets.
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Xu L, Si Y, Guo Z, Bokov D. RETRACTED: Optimal skin cancer detection by a combined ENN and Fractional Order Coot Optimization Algorithm. Proc Inst Mech Eng H 2022:9544119221113180. [PMID: 35876219 DOI: 10.1177/09544119221113180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Lina Xu
- College of Instrument Science and Electrical Engineering, Jilin University, Changchun, China
- Zhuhai College of Science and Technology, Zhuhai, China
| | - Yujuan Si
- College of Instrument Science and Electrical Engineering, Jilin University, Changchun, China
- Zhuhai College of Science and Technology, Zhuhai, China
| | - Zhiqiang Guo
- Zhuhai College of Science and Technology, Zhuhai, China
| | - Dmitry Bokov
- Institute of Pharmacy, Sechenov First Moscow State Medical University, Moscow, Russian Federation
- Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow, Russian Federation
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14
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food. J FOOD QUALITY 2022. [DOI: 10.1155/2022/5890666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and
values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.
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15
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Fatima, Imran M, Ullah A, Arif M, Noor R. A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network. Comput Biol Med 2022; 145:105424. [DOI: 10.1016/j.compbiomed.2022.105424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
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16
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Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
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Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
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17
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Uncertainty-aware skin cancer detection: The element of doubt. Comput Biol Med 2022; 144:105357. [DOI: 10.1016/j.compbiomed.2022.105357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/25/2022] [Accepted: 02/25/2022] [Indexed: 11/22/2022]
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18
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Li G, Jimenez G. Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm. Open Med (Wars) 2022; 17:508-517. [PMID: 35350832 PMCID: PMC8919846 DOI: 10.1515/med-2022-0439] [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: 06/29/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/15/2022] Open
Abstract
When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body’s lymphatic system. Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye. This makes us to provide an artificial intelligence-based methodology to diagnose this cancer with higher accuracy. This article proposes a new non-destructive testing method based on the AlexNet and Extreme Learning Machine network to provide better results of the diagnosis. The method is then optimized based on a new improved version of the Grasshopper optimization algorithm (GOA). Simulation of the proposed method is then compared with some different state-of-the-art methods and the results showed that the proposed method with 98% accuracy and 93% sensitivity has the highest efficiency.
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Affiliation(s)
- Gengluo Li
- School of Information Engineering, NanChang University , NanChang , Jiangxi 330031 , China
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19
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Applying particle swarm optimization-based decision tree classifier for wart treatment selection. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00348-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractWart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.
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20
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Slade S, Zhang L, Yu Y, Lim CP. An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06947-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractStill image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.
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21
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Jain A, Rao ACS, Jain PK, Abraham A. Multi-type skin diseases classification using OP-DNN based feature extraction approach. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:6451-6476. [PMID: 35035267 PMCID: PMC8752183 DOI: 10.1007/s11042-021-11823-x] [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: 07/13/2021] [Revised: 10/20/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is tremendously hard because of the complexities like skin tone and color variation due to the presence of hair regions. Therefore the type of skin disease prediction is not accurately achieved in many pieces of research. To deal with mentioned concerns, a novel optimal probability-based deep neural network is proposed to assist medical professionals in appropriately diagnosing the type of skin disease. Initially, the input dataset is fed into the pre-processing stage, which helps to remove unwanted contents in the image. Afterward, features extracted for all the pre-processed images are subjected to the proposed Optimal Probability-Based Deep Neural Network (OP-DNN) for the training process. This classification algorithm classifies incoming clinical images as different skin diseases with the help of probability values. While learning OP-DNN, it is essential to determine the optimal weight values for reducing the training error. For optimizing weight in OP-DNN structure, an optimization approach is implemented in this research. For that, whale optimization is utilized because it works faster than other methods. The proposed multi-type skin disease prediction model is implemented in MatLab software and achieved 95% of accuracy, 0.97 of specificity, and 0.91 of sensitivity. This exposes the superiority of the proposed multi-type skin disease prediction model using an effective OP-DNN based feature extraction approach to attain a high accuracy rate and also it predict several kinds of skin disease than the previous models, which can protect the patients survives as well as can assist the physicians in making a decision certainly.
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Affiliation(s)
- Arushi Jain
- Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004 India
| | - Annavarapu Chandra Sekhara Rao
- Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004 India
| | - Praphula Kumar Jain
- Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004 India
| | - Ajith Abraham
- Machine Intelligence Research Labs (MIR Labs), Auburn, WA 98071 USA
- Center for Artificial Intelligence, Innopolis University, Innopolis, Russia
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22
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Skin cancer detection using Kernel Fuzzy C-means and Developed Red Fox Optimization algorithm. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103160] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19). Comput Biol Med 2021; 139:104994. [PMID: 34749098 PMCID: PMC8558149 DOI: 10.1016/j.compbiomed.2021.104994] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 12/16/2022]
Abstract
COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease within communities and to commence the management of infected patients early, positive cases should be diagnosed as quickly as possible. New results from X-ray imaging indicate that images provide key information about COVID-19. Advanced deep-learning (DL) models can be applied to X-ray radiological images to accurately diagnose this disease and to mitigate the effects of a shortage of skilled medical personnel in rural areas. However, the performance of DL models strongly depends on the methodology used to design their architectures. Therefore, deep neuroevolution (DNE) techniques are introduced to automatically design DL architectures accurately. In this paper, a new paradigm is proposed for the automated diagnosis of COVID-19 from chest X-ray images using a novel two-stage improved DNE Algorithm. The proposed DNE framework is evaluated on a real-world dataset and the results demonstrate that it provides the highest classification performance in terms of different evaluation metrics.
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24
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Lawrence T, Zhang L, Rogage K, Lim CP. Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization. SENSORS (BASEL, SWITZERLAND) 2021; 21:7936. [PMID: 34883940 PMCID: PMC8659893 DOI: 10.3390/s21237936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/17/2022]
Abstract
Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.
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Affiliation(s)
- Tom Lawrence
- Department of Computer and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK; (T.L.); (K.R.)
| | - Li Zhang
- Department of Computer Science, Royal Holloway, University of London, Egham TW20 0EX, UK
| | - Kay Rogage
- Department of Computer and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK; (T.L.); (K.R.)
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia;
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25
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Maurya R, Pathak VK, Dutta MK. Deep learning based microscopic cell images classification framework using multi-level ensemble. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106445. [PMID: 34627021 DOI: 10.1016/j.cmpb.2021.106445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Advancement of the ultra-fast microscopic images acquisition and generation techniques give rise to the automated artificial intelligence (AI)-based microscopic images classification systems. The earlier cell classification systems classify the cell images of a specific type captured using a specific microscopy technique, therefore the motivation behind the present study is to develop a generic framework that can be used for the classification of cell images of multiple types captured using a variety of microscopic techniques. METHODS The proposed framework for microscopic cell images classification is based on the transfer learning-based multi-level ensemble approach. The ensemble is made by training the same base model with different optimisation methods and different learning rates. An important contribution of the proposed framework lies in its ability to capture different granularities of features extracted from multiple scales of an input microscopic cell image. The base learners used in the proposed ensemble encapsulates the aggregation of low-level coarse features and high-level semantic features, thus, represent the different granular microscopic cell image features present at different scales of input cell images. The batch normalisation layer has been added to the base models for the fast convergence in the proposed ensemble for microscopic cell images classification. RESULTS The general applicability of the proposed framework for microscopic cell image classification has been tested with five different public datasets. The proposed method has outperformed the experimental results obtained in several other similar works. CONCLUSIONS The proposed framework for microscopic cell classification outperforms the other state-of-the-art classification methods in the same domain with a comparatively lesser amount of training data.
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Affiliation(s)
- Ritesh Maurya
- Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India.
| | | | - Malay Kishore Dutta
- Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India.
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26
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Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
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27
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Jalali SMJ, Ahmadian M, Ahmadian S, Khosravi A, Alazab M, Nahavandi S. An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis. Appl Soft Comput 2021; 111:107675. [PMID: 34305489 PMCID: PMC8272021 DOI: 10.1016/j.asoc.2021.107675] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 05/08/2021] [Accepted: 06/28/2021] [Indexed: 12/19/2022]
Abstract
A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. Therefore, an effective computerized system must be established as a matter of urgency, to facilitate health care professionals in recognizing X-ray images from COVID-19 patients. In this work, we design a novel artificial intelligent-based automated X-ray image analysis framework based on an ensemble of deep optimized convolutional neural networks (CNNs) in order to distinguish coronavirus patients from non-patients. By developing a modified version of gaining-sharing knowledge (GSK) optimization algorithm using the Opposition-based learning (OBL) and Cauchy mutation operators, the architectures of the deployed deep CNNs are optimized automatically without performing the general trial and error procedures. After obtaining the optimized CNNs, it is also very critical to identify how to decrease the number of ensemble deep CNN classifiers to ensure the classification effectiveness. To this end, a selective ensemble approach is proposed for COVID-19 X-ray based image classification using a deep Q network that combines reinforcement learning (RL) with the optimized CNNs. This approach increases the model performance in particular and therefore decreases the ensemble size of classifiers. The experimental results show that the proposed deep RL optimized ensemble approach has an excellent performance over two popular X-ray image based COVID-19 datasets. Our proposed advanced algorithm can accurately identify the COVID-19 patients from the normal individuals with a significant accuracy of 0.991441, precision of 0.993568, recall (sensitivity) of 0.981445, F-measure of 0.989666 and AUC of 0.990337 for Kaggle dataset as well as an excellent accuracy of 0.987742, precision of 0.984334, recall (sensitivity) of 0.989123, F-measure of 0.984939 and AUC of 0.988466 for Mendely dataset.
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Affiliation(s)
| | - Milad Ahmadian
- Deparment of Computer Engineering, Razi University, Kermanshah, Iran
| | - Sajad Ahmadian
- Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
| | - Mamoun Alazab
- College of Engineering, IT and Environment, Charles Darwin University, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
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28
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Cheong KH, Tang KJW, Zhao X, Koh JEW, Faust O, Gururajan R, Ciaccio EJ, Rajinikanth V, Acharya UR. An automated skin melanoma detection system with melanoma-index based on entropy features. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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29
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Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106918] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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30
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Kara A. A data-driven approach based on deep neural networks for lithium-ion battery prognostics. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05976-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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31
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KUMAR TIWARI ABHINANDAN, KUMAR MISHRA MANOJ, RANJAN PANDA AMIYA, PANDA BIKRAMADITYA. HOSMI-LBP-BASED FEATURE EXTRACTION FOR MELANOMA DETECTION USING HYBRID DEEP LEARNING MODELS. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
“Melanoma is a serious form of skin cancer that begins in cells known as melanocytes and more dangerous due to its spreading ability to other organs more rapidly if it is not treated at an early stage”. This paper aims to propose a Melanoma detection methodology that includes four major phases: “(i) pre-processing (ii) segmentation (iii) the proposed feature extraction and (iv) classification”. Initially, pre-processing is performed, where the input image is subjected to processing like resizing and edge smoothening. Subsequently, segmentation is carried out by the Otsu thresholding process. In the feature extraction phase, the proposed Higher-Order Standardized Moment Induced-Local Binary Patterns (HOSMI-LBP)-based features are extracted. These features are then subjected to a classification process for classifying the disease. For this, it is planned to use a hybrid classification framework, where the Convolutional Neural Network (CNN) and the Neural Network (NN) are deployed. Two-phase of classification gets processed: the extracted features are subjected to NN; the input image is directly classified using an optimized CNN framework. Finally, the classified outputs from NN and optimized CNN are averaged and the final output is considered as detected output. Particularly, the weight and initial rate of CNN is optimized using the proposed algorithm known as the Sea Lion Integrated Grey Wolf Algorithm (SLI-GWO) method that hybrid the concepts of both Sea Lion Optimization (SLnO) and Grey Wolf Optimization (GWO) algorithm. At last, the proposed work performance is computed with traditional systems in terms of various measures.
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Affiliation(s)
- ABHINANDAN KUMAR TIWARI
- School of Computer Engineering, Kalinga Institute of Industrial Technology, KIIT University, Campus 15 Road, Chandaka Industrial Estate, Patia, Bhubaneswar, Odisha 751024, India
| | - MANOJ KUMAR MISHRA
- School of Computer Engineering, Kalinga Institute of Industrial Technology, KIIT University, Campus 15 Road, Chandaka Industrial Estate, Patia, Bhubaneswar, Odisha 751024, India
| | - AMIYA RANJAN PANDA
- School of Computer Engineering, Kalinga Institute of Industrial Technology, KIIT University, Campus 15 Road, Chandaka Industrial Estate, Patia, Bhubaneswar, Odisha 751024, India
| | - BIKRAMADITYA PANDA
- School of Computer Engineering, Kalinga Institute of Industrial Technology, KIIT University, Campus 15 Road, Chandaka Industrial Estate, Patia, Bhubaneswar, Odisha 751024, India
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Xie H, Zhang L, Lim CP, Yu Y, Liu H. Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models. SENSORS 2021; 21:s21051816. [PMID: 33807806 PMCID: PMC7961412 DOI: 10.3390/s21051816] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022]
Abstract
In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.
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Affiliation(s)
- Hailun Xie
- Computational Intelligence Research Group, Department of Computer and Information Sciences, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK;
| | - Li Zhang
- Computational Intelligence Research Group, Department of Computer and Information Sciences, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK;
- Correspondence:
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia;
| | - Yonghong Yu
- College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing 210049, China;
| | - Han Liu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
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Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review. SENSORS 2021; 21:s21010252. [PMID: 33401739 PMCID: PMC7795742 DOI: 10.3390/s21010252] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 02/04/2023]
Abstract
The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.
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Sukanya ST, Jerine. A novel melanoma detection model: adapted K-means clustering-based segmentation process. BIO-ALGORITHMS AND MED-SYSTEMS 2020. [DOI: 10.1515/bams-2020-0040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Abstract
Objectives
The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids.
Methods
This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection.
Results
Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively.
Conclusions
From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.
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Affiliation(s)
- S. T. Sukanya
- Noorul Islam Centre for Higher Education , Kanyakumari , India
| | - Jerine
- Noorul Islam Centre for Higher Education , Kanyakumari , India
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Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106336] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106328] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Lawrence T, Zhang L. IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices. SENSORS (BASEL, SWITZERLAND) 2019; 19:E5541. [PMID: 31847434 PMCID: PMC6960729 DOI: 10.3390/s19245541] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 12/03/2019] [Accepted: 12/11/2019] [Indexed: 11/21/2022]
Abstract
Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.
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Affiliation(s)
| | - Li Zhang
- Department of Computer and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;
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