Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Abdar, Moloud; Samami, Maryam; Mahmoodabad, Sajjad Dehghani; Doan, Thang; Mazoure, Bogdan; Hashemifesharaki, Reza; Liu, Li; Khosravi, Abbas; Acharya, U. Rajendra; Makarenkov, Vladimir; Nahavandi, Saeid

doi:10.1016/j.compbiomed.2021.104418

Cited by 140 publications

(55 citation statements)

References 56 publications

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“…As per the viewpoint of Abdar and other researchers, despite the high accuracy level of DL, there are significant limitations of these methods as the classifications sometimes can have "disastrous consequences" [8]. Researchers have proven that the "Naive Bayes" DL method is a significant AI algorithm that can effectively classify and segment medical images.…”

Section: Literature Reviewmentioning

confidence: 99%

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

C.¹,

G²,

Sajitha³

et al. 2022

Journal of Food Quality

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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 p 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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Section: Literature Reviewmentioning

confidence: 99%

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

C.¹,

G²,

Sajitha³

et al. 2022

Journal of Food Quality

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show abstract

“…Hence, proper uncertainty estimations are essential to improve the efficacy of the model and apply to medical domain with trust and reliability. Proper UQ is crucial to enhance interpretability and trust in machine and deep learning models, which is significantly important in various healthcare applications [25], [26]. Trustworthy uncertainty estimations can facilitate informing clinical decision making, and more importantly, prepare clinicians with appropriate feedback on when to relinquish automatically obtained results.…”

Section: B Uncertainty Quantification (Uq)mentioning

confidence: 99%

UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

Abdar¹,

Salari²,

Qahremani³

et al. 2021

Preprint

Self Cite

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The COVID-19 (Coronavirus disease 2019) has infected more than 151 million people and caused approximately 3.17 million deaths around the world up to the present. The rapid spread of COVID-19 is continuing to threaten human's life and health. Therefore, the development of computer-aided detection (CAD) systems based on machine and deep learning methods which are able to accurately differentiate COVID-19 from other diseases using chest computed tomography (CT) and X-Ray datasets is essential and of immediate priority. Different from most of the previous studies which used either one of CT or X-ray images, we employed both data types with sufficient samples in implementation. On the other hand, due to the extreme sensitivity of this pervasive virus, model uncertainty should be considered, while most previous studies have overlooked it. Therefore, we propose a novel powerful fusion model named Uncer t ai nt yF useNet that consists of an uncertainty module: Ensemble Monte Carlo (EMC) dropout. The obtained results prove the effectiveness of our proposed fusion for COVID-19 detection using CT scan and X-Ray datasets. Also, our proposed Uncer t ai nt yF useNet model is significantly robust to noise and performs well with the previously unseen data. The source codes and models of this study are available at: https://github.com/ moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

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“…The two most widely used epistemic UQ methods include Bayesian approximation (Abdar, Samami, et al, 2021) and ensemble methods. The Bayesian deep learning approach is concerned with the development of deep learning models which quantify epistemic uncertainty.…”

Section: Contribution and Limitationsmentioning

confidence: 99%

Sequential multi‐headed attention for entity‐based relational neural networks

Moges

Zhao

2022

Expert Systems

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The capacity of relational interaction between high-level information and reason is the defining characteristic of human intelligence. Regardless of the remarkable progress in artificial intelligence, recent machine reading comprehension models still heavily rely on high-dimensional word-based distributed representations. Since these models employ statistical means to answer questions of complex textual corpus and employ an accuracy-based metric system, their learning capacity of the required skills is not guaranteed. To ensure the capacity of MRC models to learn the desired skills, explainability has become an emerging requirement. In this paper, we propose an end-to-end natural language reasoning model that is based on sets of high-level aggregated representations which promote operational explainability. To this end, sequential multi-head attention, and a loss regularization function is proposed. We show analysis of the proposed approach on two natural language reasoning oriented question and answering datasets (bAbI and NewsQA).

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Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Cited by 140 publications

References 56 publications

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

Sequential multi‐headed attention for entity‐based relational neural networks

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