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Ali MU, Khalid M, Alshanbari H, Zafar A, Lee SW. Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework. Bioengineering (Basel) 2023; 10:1430. [PMID: 38136020 PMCID: PMC10741172 DOI: 10.3390/bioengineering10121430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/07/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed-trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.
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Affiliation(s)
- Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea;
| | - Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computers, Umm Al-Qura University, Makkah 21955, Saudi Arabia; (M.K.); (H.A.)
| | - Hanan Alshanbari
- Department of Computer Science and Artificial Intelligence, College of Computers, Umm Al-Qura University, Makkah 21955, Saudi Arabia; (M.K.); (H.A.)
| | - Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea;
| | - Seung Won Lee
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
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Jiang X, Fan J, Zhu Z, Wang Z, Guo Y, Liu X, Jia F, Dai C. Cybersecurity in neural interfaces: Survey and future trends. Comput Biol Med 2023; 167:107604. [PMID: 37883851 DOI: 10.1016/j.compbiomed.2023.107604] [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: 06/01/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.
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Affiliation(s)
- Xinyu Jiang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Jiahao Fan
- The Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ziyue Zhu
- The Department of Bioengineering, Imperial College London, SW7 2AZ London, UK
| | - Zihao Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yao Guo
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangyu Liu
- The College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Fumin Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Chenyun Dai
- School of Information Science and Technology, Fudan University, Shanghai, China.
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Chu SC, Shao ZY, Zhong N, Liu GG, Pan JS. An Enhanced Food Digestion Algorithm for Mobile Sensor Localization. SENSORS (BASEL, SWITZERLAND) 2023; 23:7508. [PMID: 37687962 PMCID: PMC10490790 DOI: 10.3390/s23177508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023]
Abstract
Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.
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Affiliation(s)
- Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
- College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, SA 5042, Australia
| | - Zhi-Yuan Shao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
| | - Ning Zhong
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan;
- International WIC Institute, Beijing University of Technology, Beijing 100124, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China
| | - Geng-Geng Liu
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
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Zafar A, Tanveer J, Ali MU, Lee SW. BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization. Bioengineering (Basel) 2023; 10:825. [PMID: 37508852 PMCID: PMC10376009 DOI: 10.3390/bioengineering10070825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/04/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Peksa J, Mamchur D. State-of-the-Art on Brain-Computer Interface Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:6001. [PMID: 37447849 DOI: 10.3390/s23136001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.
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Affiliation(s)
- Janis Peksa
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Institute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
| | - Dmytro Mamchur
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Computer Engineering and Electronics Department, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600 Kremenchuk, Ukraine
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Ali MU, Kim KS, Kallu KD, Zafar A, Lee SW. OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface. Bioengineering (Basel) 2023; 10:608. [PMID: 37237678 PMCID: PMC10215946 DOI: 10.3390/bioengineering10050608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.
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Affiliation(s)
- Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea;
| | - Kwang Su Kim
- Department of Scientific Computing, Pukyong National University, Busan 48513, Republic of Korea;
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan
| | - Karam Dad Kallu
- Department of Robotics & Artificial Intelligence (R&AI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H-12, Islamabad 44000, Pakistan;
| | - Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea;
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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