Breast cancer diagnosis using deep belief networks on ROI images

ALTAN, Gökhan

doi:10.5505/pajes.2021.38668

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Furthermore, the other studies also used relatively small sample sizes which limited generalization. They were conducted in the different settings: Gokhan Altan extracted the 21–24 h long-term ECGs of 60 subjects diagnosed with CAD from the Long-Term ST Database [ 28 , 29 ] and Monappa Gundappa Poddar gathered ECG data of 64 male patients in the age group of 35–60 years who were previously healthy in India [ 30 ]. Therefore, a small sample size yielded less variation in the ECG data.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Choi¹,

Lee²,

Park³

et al. 2023

BMC Cardiovasc Disord

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Background Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. Methods ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. Results The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. Conclusions ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.

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Section: Discussionmentioning

confidence: 99%

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Choi¹,

Lee²,

Park³

et al. 2023

BMC Cardiovasc Disord

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“…Apart from CNN, several different deep learning models such as Deep Belief Networks (DBN), recurrent neural networks (RNN), and deep Boltzmann machines (DBM) have also been used in medical applications such as disease diagnosis. In [53], Altan et al proposed a methodology to detect breast cancer using DBN in which different medical images are used as a dataset. To detect breast cancer, statistical and physiological features are extracted from the images used in the dataset.…”

Section: Machine Learning-based Disease Detectionmentioning

confidence: 99%

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Rehman

Shafique

Ghadi

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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“…Numerous studies have been carried out. According to the data types used in study, these studies can be divided into studies based on the ECG signals [11][12][13][14][15][16][17][18][19][20][21][22], studies based on the imaging data [23][24][25][26][27][28][29], and studies based on the data of multiple routine examination items. As mentioned above, many imaging examinations are difficult to popularize in some countries and regions.…”

Section: Introductionmentioning

confidence: 99%

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

et al. 2022

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Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.

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Breast cancer diagnosis using deep belief networks on ROI images

Cited by 8 publications

References 25 publications

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

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