Detection of unexpected findings in radiology reports: A comparative study of machine learning approaches

López-Úbeda, Pilar; Díaz–Galiano, Manuel Carlos; Martín-Noguerol, Teodoro; Ureña-López, Alfonso; Martín–Valdivia, María Teresa; Luna, Antonio

doi:10.1016/j.eswa.2020.113647

Cited by 22 publications

(11 citation statements)

References 30 publications

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“…The authors used 800 chest X-ray images taken from two public datasets named Montgomery and Shenzhen. López-Úbeda et al ( 2020 ) explored the machine learning methods to detect tuberculosis in Spanish radiology reports. They also mentioned the deep learning classification algorithms with the purpose of its evaluation and comparison and to carry such a task.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

490

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

490

173

View full text Add to dashboard Cite

show abstract

“…Then, it calculates the posterior probability for each class using the Bayesian equation, and the class with the highest posterior probability is the outcome of the prediction. Naïve Bayes is considered one of the most efficient inductive machine learning algorithm (López-Úbeda et al, 2020).…”

Section: Naïve Bayesmentioning

confidence: 99%

A Comparison of Classification Models in Predicting Graduate Admission Decision

2021

JHETP

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While the decision process of graduate admissions remains elusive, specific criteria are decidedly significant in determining admission outcome. Prospective students applying to graduate programs experience a real predicament of selecting the right schools to invest limited resources for the application. This paper presents comparisons of various machine learning classification models, including Naïve Bayes, Logistic Regression, Multilayer Perceptron and Decisions Tree models, in predicting the admission outcome of candidates with a set of known parameters using a dataset of 400 applicant records. By comparing the performance metrics of these methods, the study finds Naïve Bayes to be the most accurate model for this type of dataset. Predictive models such as the ones discussed in this paper can be a valuable tool for prospective students in shortlisting universities in their application process. The study also proposes a framework that incorporates machine learning-based classification into the admissions decision process. Implementing such methods may help support graduate admissions committees in streamlining large pools of applications or observing and understanding trends in their past admission decisions.

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“…The CNN performance interpretation is an urgent task, especially in clinical decision-making [27,44]. DL models have been considered black boxes for a long time; there is still no trust in their forecasts [18,33]. Simultaneously, understanding the principles of feature detection can help configure and optimize network hyperparameters, identify and understand the cause of model failures, and explain the results to a non-specialist user in solving practical problems.…”

Section: Visual Analysis Through Discriminative Localizationmentioning

confidence: 99%

An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information Technology

Радюк¹,

Barmak²,

Krak³

2021

TOBIOIJ

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Aim: This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays. Background: For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment. Objective: The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis. Methods: Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features. Results: An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making. Conclusion: According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.

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Detection of unexpected findings in radiology reports: A comparative study of machine learning approaches

Cited by 22 publications

References 30 publications

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

A Comparison of Classification Models in Predicting Graduate Admission Decision

An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information Technology

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