Browsing by Author "Karim, Ahmad M."

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A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing
(2019) Karim, Ahmad M.; Guzel, Mehmet S.; Tolun, Mehmet R.; Kaya, Hilal; Celebi, Fatih V.
This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
(2018) Tolun, Mehmet R.; Karim, Ahmad M.; Guzel, Mehmet S.; Kaya, Hilal; Celebi, Fatih V.; 0000-0002-8478-7220; AAX-2456-2021
Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.
Proton Therapy for Mandibula Plate Phantom
(2021) Senirkentli, Guler Burcu; Ekinci, Fatih; Bostanci, Erkan; Guezel, Mehmet Serdar; Dagli, Ozlem; Karim, Ahmad M.; Mishra, Alok; 33557337
Purpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values. Methods: In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom. Results: Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47-3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased. Conclusion: These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues.