Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

Cardoso, Isadora Bianco; Almeida, Eliana Silva de; Allende‐Cid, Héctor; Frery, Alejandro C.; Rangayyan, Rangaraj M.; Azevedo-Marques, Paulo Mazzoncini de; Ramos, Heitor S.

doi:10.3414/me17-02-0023

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Taking advantage of the large number of images generated by digital processing [3], some studies [5][6][7][8][9] focus on methods based on convolutional neural networks (CNN) to de ne whether a patient has pneumonia or not since they learn and select functions automatically. Other works [10][11][12][13] highlight the analysis of cracks using arti cial neural networks, hidden Markov model, modeling of Gaussian mixtures (Gaussian Mixture Models, GMM), and algorithm K-NN (K-Nearest Neighbors).…”

Section: Introductionmentioning

confidence: 99%

Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform

Alhazmi

Alassery

2022

Computational Intelligence and Neuroscience

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Pneumonia is a disease that spreads quickly and poses a serious risk to the health and well-being of its victims. An accurate biomedical diagnosis of pneumonia necessitates the use of various diagnostic tools and the evaluation of various clinical features, all of which are hindered by the lack of available experts and tools. According to the research presented here, a mobile app that uses deep learning techniques to classify whether or not a patient has pneumonia is being developed. It was hoped that a mobile application prototype for detecting pneumonia using neural networks would be developed as part of this study. The use of a high-level tool such as Create ML makes this process easier and eliminates issues such as how many layers a neural network has, initializing the model parameters, or which algorithms to use. The model can now be accessed by anyone, anywhere, via a mobile application. The dataset of more than 5,000 real images was used to train an image classification model using Create ML, a tool with a graphical interface, and there was no need for specialized knowledge.

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Section: Introductionmentioning

confidence: 99%

Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform

Alhazmi

Alassery

2022

Computational Intelligence and Neuroscience

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Computational Intelligence Re-meets Medical Image Processing

Costin

Deserno

2018

Methods Inf Med

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Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

2020

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Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.

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Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

Cited by 3 publications

References 17 publications

Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform

Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform

Computational Intelligence Re-meets Medical Image Processing

Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

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