Ischemic heart disease detection using selected machine learning methods

Ciecholewski, Marcin

doi:10.1080/00207160.2012.742189

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…The developed algorithm was run in Google Colab [21,30] runtime environment, which gives access to a powerful machine with faster GPUs and an increased amount of RAM and disk, making it suitable for training large-scale ML and DL models. This platform provides free space for uploading data, allowing users to run code entirely on the cloud and thus, overcoming any computational limitations on local machines.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed model was evaluated with the utilization of the cross-validation approach and achieved an AUC of 0.853%, accuracy of 0.938%, and sensitivity of 0.963%. Ciecholewski et al in [21] presented three methodologies: SVM, PCA (principal component algorithm) and NN, to diagnose ischemic heart disease. The results showed that SVM achieved greater accuracy (92.31%) and specificity (98%), whereas PCA extracted the best sensitivity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Παπανδριανός

Feleki

Papageorgiou

et al. 2022

JCM

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(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Παπανδριανός

Feleki

Papageorgiou

et al. 2022

JCM

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“…Unlike the works [5,7,8], where the achieved accuracy of diagnostic results is 85 %, proposed in the framework of the IEI-technology modification of the sequential directed selection of the dictionary of features provides the estimated by the training matrix probability of making correct diagnostic decision equal to true P 0,999, = which corresponds to two incorrectly classified vectors of the training sample. At this, the proposed modification of the cluster algorithm of the selection of the dictionary of features with using information-extreme machine training allows obtaining the faultless by the training matrix decisive rules.…”

Section: Discussion Of the Results Of Physical Simulationmentioning

confidence: 99%

“…However, the accuracy of the obtained decision rules with different kernels of SVM classifier did not exceed 81 %, which is associated with heterogeneous distribution of the vectors of the sample and the intersection of the classes in the feature space. In the works [5,8], it was offered to use the method of principal components and the algorithm Relief -F for reduction of the dictionary of features containing category features, both quantitative and converted with the use of Dummy-coding of category features. However, obtained by the traditional algorithms (SVM, J 4.8, Bayes Net and Naive Bayes), decision rules are not characterized by high accuracy as a result of ignoring non-linear structural image relations and the presence of category features.…”

Section: Analysis Of Scientific Literature and The Problem Statementmentioning

confidence: 99%

Designing algorithms for optimization of parameters of functioning of intelligent system for radionuclide myocardial diagnostics

Dovbysh

Moskalenko

Москаленко

et al. 2016

EEJET

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Розроблено інформаційно-екстремальні алгоритми машинного навчання системи радіонуклідного діагностування міокарду з оптимізацією словника ознак розпізнавання за інформаційним критерієм Кульбака, що дозволяє підвищити точність діагностичних рішень. Досліджено послідовні спрямовані та ройові алгоритми селекції словника ознак, який містить як кількісні, так і категоріальні ознаки. Отримано безпомилкові за навчальною матрицею вирішальні правила Ключові слова: сцинтиграфія, перетворення Фур'є, інформаційний критерій, машинне навчання, ройовий алгоритм Разработаны информационно-экстремальные алгоритмы машинного обучения системы радионуклидного диагностирования миокарда с оптимизацией словаря признаков по информационным критериям Кульбака, что позволяет повысить точность диагностических решений. Исследованы последовательно направленные и роевые алгоритмы селекции словаря признаков, который содержит как количественные, так и категориальные признаки. Получены безошибочные по обучающей матрице решающие правила Ключевые слова: сцинтиграфия, преобразования Фурье, информационный критерий, машинное обучение, роевой алгоритм

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“…Therefore, AI cardiac SPECT has attracted a great deal of attention in the last few years. In 2013, Ciecholewski presented a high-efficient SVM method to diagnose ischemic heart disease (45). Heart images acquired by SPECT were classified by the SVM system with a higher accuracy than using principal component analysis (PCA) and NNs.…”

Section: Cardiac Imagingmentioning

confidence: 99%

Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review

Shao¹,

Rowe²,

Du³

2021

Ann Transl Med

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Artificial intelligence (AI) has been widely applied to medical imaging. The use of AI for emission computed tomography, particularly single-photon emission computed tomography (SPECT) emerged nearly 30 years ago but has been accelerated in recent years due to the development of AI technology. In this review, we will describe and discuss the progress of AI technology in SPECT imaging. The applications of AI are dispersed in disease prediction and diagnosis, post-reconstruction image denoising, attenuation map generation, and image reconstruction. These applications are relevant to many disease categories such as the neurological disorders, kidney failure, cancer, heart disease, etc. This review summarizes these applications so that SPECT researchers can have a reference overview of the role of AI in current SPECT studies. For each application, we followed the timeline to present the evolution of AI's usage and offered insights on how AI was combined with the knowledge of underlying physics as well as traditional nonlearning techniques. Ultimately, AI applications are critical to the progress of modern SPECT technology because they provide compensations for many deficiencies in conventional SPECT imaging methods and demonstrate unparalleled success. Nonetheless, AI also has its own challenges and limitations in the medical field, including SPECT imaging. These fundamental questions are discussed, and possible future directions and countermeasures are suggested.

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Ischemic heart disease detection using selected machine learning methods

Cited by 8 publications

References 15 publications

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Designing algorithms for optimization of parameters of functioning of intelligent system for radionuclide myocardial diagnostics

Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review

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