A Proposal for an Explainable Fuzzy-based Deep Learning System for Skin Cancer Prediction

Lima, Servio; Terán, Luis; Portmann, Edy

doi:10.1109/icedeg48599.2020.9096799

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…It must be considered, however, that in the context of critical systems where errors are not allowed, such as in the medical field, there is an increasing demand for the comprehensibility of the algorithms used. This area of proposing models that can explain their own behavior is known as explainable AI (XAI) and has been the subject of numerous studies in recent years, including in the area of skin lesion classification [142][143][144][145][146]. In order for physicians to trust AI, the way in which machines make decisions must be made clear.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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“…In an effort to reach more veterans with post-traumatic stress disorder (PTSD), the U.S. Government calculated the yearly and cumulative uptake and prevalence rates from 2001 to 2014 for each of the two EBPs for PTSD using NLP [83]. Furthermore, NLP can be used to create applications that can be used by governments to put in place prevention-focused policies and lower the cost of the disease's total treatment [84]. To summarise, NLP techniques both lower costs and raise the standard of healthcare [7].…”

Section: Nlp For Medical and Healthcarementioning

confidence: 99%

Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review

Jiang,

Pang,

Wong

et al. 2023

Applied Sciences

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Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.

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“…Explainable models are able to communicate how and why a specific recommendation or outcome has been derived [33]. Deep learning models may include anywhere from several to hundreds of layers and are best known for extracting high-level abstract features.…”

Section: Explainabilitymentioning

confidence: 99%

Reducing Kidney Discard With Artificial Intelligence Decision Support: the Need for a Transdisciplinary Systems Approach

et al. 2021

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Purpose of Review A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard. Recent Findings Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives. Summary Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.

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A Proposal for an Explainable Fuzzy-based Deep Learning System for Skin Cancer Prediction

Cited by 11 publications

References 39 publications

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review

Reducing Kidney Discard With Artificial Intelligence Decision Support: the Need for a Transdisciplinary Systems Approach

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