Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

Wang, Zhiqiong; Luo, Yiqi; Xin, Junchang; Zhang, Hao; Qu, Liangsheng; Wang, Zhongyang; Yao, Yu-Dong; Zhu, Wancheng; Wang, Xingwei

doi:10.1109/access.2020.3012093

Cited by 16 publications

(8 citation statements)

References 69 publications

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“…Wang et al [24] reviewed the application of ELM in computer-aided diagnostics (CAD). The authors showed it is possible to apply ELM in the construction of CAD systems, so the perspective is broad and deserves further study.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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“…This barrier was often mentioned (n = 48) as it requires appropriate data labelling supported by reliable annotation methods, which can be a very demanding process (need for trained staff to label or verify the data). This process was qualified as expensive, time-consuming, and subjective if not adequately performed [18,19,[23][24][25][26][27][28].…”

Section: B1 Datamentioning

confidence: 99%

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

Lokaj,

Pugliese,

Kinkel

et al. 2023

Eur Radiol

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Objective Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. Method A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. Results A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). Conclusion This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. Clinical relevance statement The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. Key Points • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

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“…2024, 14, 4005 2 of 20 the development of automated and precise dermatological lesion identification systems assumes paramount importance [5]. Computer-aided diagnosis (CAD) systems have made substantial strides in identifying and assessing various malignancies [6], spanning lung cancer [7], breast cancer [8], thyroid cancer [9], brain cancer [10], and liver cancer [11], among others. In the domain of skin cancer detection, CAD system implementation becomes indispensable, enhancing efficiency, curtailing time and costs, and compensating for the scarcity of dermatologists.…”

Section: Introductionmentioning

confidence: 99%

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

Tang,

Su,

Chen

et al. 2024

Applied Sciences

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In recent decades, skin cancer has emerged as a significant global health concern, demanding timely detection and effective therapeutic interventions. Automated image classification via computational algorithms holds substantial promise in significantly improving the efficacy of clinical diagnoses. This study is committed to mitigating the challenge of diagnostic accuracy in the classification of multiclass skin lesions. This endeavor is inherently formidable owing to the resemblances among various lesions and the constraints associated with extracting precise global and local image features within diverse dimensional spaces using conventional convolutional neural network methodologies. Consequently, this study introduces the SkinSwinViT methodology for skin lesion classification, a pioneering model grounded in the Swin Transformer framework featuring a global attention mechanism. Leveraging the inherent cross-window attention mechanism within the Swin Transformer architecture, the model adeptly captures local features and interdependencies within skin lesion images while additionally incorporating a global self-attention mechanism to discern overarching features and contextual information effectively. The evaluation of the model’s performance involved the ISIC2018 challenge dataset. Furthermore, data augmentation techniques augmented training dataset size and enhanced model performance. Experimental results highlight the superiority of the SkinSwinViT method, achieving notable metrics of accuracy, recall, precision, specificity, and F1 score at 97.88%, 97.55%, 97.83%, 99.36%, and 97.79%, respectively.

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Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

Cited by 16 publications

References 69 publications

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

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