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khodajou-chokami--h
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A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks
, Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term...
Monte carlo modeling of magnification mode for quantitative assessment of image quality in mammography systems
, Article 2019 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2019, 26 June 2019 through 28 June 2019 ; 2019 ; 9781538684276 (ISBN) ; Khodajou Chokami, H ; Vosoughi, N ; Noorvand, M ; IEEE; IEEE Instrumentation and Measurement Society; Kadir Has University (KHU); UME ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Scattered radiations are one of the most important factors in the degradation of the image quality of mammography systems. Some techniques have been proposed for the reduction of such rays. Magnification mode can be done by increasing the air gap which determined by the distance of the breast and image intensifier. It is one of the useful techniques to elevate the produced image quality due to the rejection of scattered photons. Monte Carlo N-Particle eXtended transport code (MCNPX) version 2.7.0 is a software package for the simulation of physical processes. In this work, this computer code is used for analyzing the effects of magnification mode on mammographic image quality. To this...