Artificial intelligence methods for predictive image-based grading of human cancers

Fernández, Gerardo; Madduri, Abishek Sainath; Marami, Bahram; Prastawa, Marcel; Scott, Richard; Zeineh, Jack; Donovan, Michael

doi:10.1016/b978-0-323-67538-3.00009-9

Cited by 4 publications

(1 citation statement)

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“…SVM is a linear method that has been extended to include non-linear problems by projecting the data to a higher dimensional space. This method has been particularly successful due to the ease with which it handles high-dimensional data and the interpretability of its linear models [ 30 ]. SVM has been found to be good for binary classification but poor for noisy data.…”

Section: Methodsmentioning

confidence: 99%

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

Botros

Mourad-Chehade

Laplanche

2022

Sensors

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Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.

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Section: Methodsmentioning

confidence: 99%