Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain

Moradi, Milad; Samwald, Matthias

doi:10.48550/arxiv.2202.12678

Cited by 2 publications

(2 citation statements)

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“…Finally, the authors propose the methodology to combat existing approaches based on three factors: reliability, resilience, and personalization. The work in [92] describes existing AI techniques like ML/DL/NLP in and extends the survey to explain the importance of EXAI in future medicine and biomedical applications. Authors in [29] implements analyze the medical ECG data on proposing a generalized model, ST-CNN-GAP-5, that implements DNN algorithms using online available two ECG datasets with the achieved accuracy of 95.8% and AUC value of 99.46%.…”

Section: State-of-the-artmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

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Section: State-of-the-artmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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“…RBMs can be trained using more effective algorithms than unrestricted Boltzmann machines. In classification applications, RBMs are frequently used for feature extraction [15]. Data imbalance, which can significantly reduce performance, is a major challenge to medical image classification, as negative instances are much smaller than positive ones.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Lung Nodules in Computerized Tomography Lung Images using a Hybrid Method with Class Imbalance Reduction

Wang¹,

Wang²,

Dong³

2023

IJACSA

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Lung cancer is among the deadly diseases affecting millions globally every year. Physicians' and radiologists' manual detection of lung nodules has low efficiency due to the variety of shapes and nodule locations. The paper aims to recognize the lung nodules in computerized tomography (CT) lung images utilizing a hybrid method to reduce the problem space at every step. First, the suggested method uses the fast and robust fuzzy cmeans clustering method (FRFCM) algorithm to segment CT images and extract two lungs, followed by a convolutional neural network (CNN) to identify the sick lung for use in the next step. Then, the adaptive thresholding method detects the suspected regions of interest (ROIs) among all available objects in the sick lung. Next, some statistical features are selected from every ROI, and then a restricted Boltzmann machine (RBM) is considered a feature extractor that extracts rich features among the selected features. After that, an artificial neural network (ANN) is employed to classify ROIs and determine whether the ROI includes nodules or non-nodules. Finally, cancerous ROIs are localized by the Otsu thresholding algorithm. Naturally, sick ROIs are more than healthy ones, leading to a class imbalance that substantially decreases ANN ability. To solve this problem, a reinforcement learning (RL)-based algorithm is used, in which the states are sampled. The agent receives a larger reward/penalty for correct/incorrect classification of the examples related to the minority class. The proposed model is compared with state-of-the-art methods on the lung image database consortium image collection (LIDC-IDRI) dataset and standard performance metrics. The results of the experiments demonstrate that the proposed model outperforms its rivals.

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Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain

Cited by 2 publications

References 0 publications

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Recognition of Lung Nodules in Computerized Tomography Lung Images using a Hybrid Method with Class Imbalance Reduction

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