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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science
El Malali, Hamid
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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New technic of transfer learning for detecting epilepsy by EfficientNet and DarkNet models Edderbali, Fatima; El Malali, Hamid; Essoukaki, Elmaati; Harmouchi, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp345-352

Abstract

Epileptic seizures are one of the most prevalent brain disorders in the world. Electroencephalography (EEG) signal analysis is used to distinguish between normal and epileptic brain activity. To date, automatic diagnosis remains a highly relevant and significant research topic which can help in this task, especially considering that such diagnosis requires a significant amount of time to be carried out by an expert. As a result, the need for an effective seizure approach capable to classify the normal and epileptic brain signal automatically is crucial. In this perspective, this work proposes a deep neural network approach using transfer learning to classify spectrogram images that have been extracted from EEG signals. Initially, spectrogram images have been extracted and used as input to pre-trained models, and a second refinement is performed on certain feature extraction layers that were previously frozen. The EfficientNet and DarkNet networks are used. To overcome the lack of data, data augmentation was also carried out. The proposed work performed excellently, as assessed by multiple metrics, such as the 0.99 accuracy achieved with EfficientNet combined with a support vector machine (SVM) classifier.
A Reproducible Workflow for Liver Volume Segmentation and 3D Model Generation Using Open-Source Tools Labakoum, Badreddine; El Malali, Hamid; Farhan, Amr; Mouhsen, Azeddine; Lyazidi, Aissam
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1086

Abstract

Complex liver resections related to hepatic tumors represent a major surgical challenge that requires precise preoperative planning supported by reliable three-dimensional (3D) anatomical models. In this context, accurate volumetric segmentation of the liver is a critical prerequisite to ensure the fidelity of printed models and to optimize surgical decision-making. This study compares different segmentation techniques integrated into open-source software to identify the most suitable approach for clinical application in resource-limited settings. Three semi-automatic methods, region growing, thresholding, and contour interpolation, were tested using the 3D Slicer platform and compared with a proprietary automatic method (Hepatic VCAR, GE Healthcare) and a manual segmentation reference, considered the gold standard. Ten anonymized abdominal CT volumes from the Medical Segmentation Decathlon dataset, encompassing various hepatic pathologies, were used to assess and compare the performance of each technique. Evaluation metrics included the Dice similarity coefficient (Dice), Hausdorff distance (HD), root mean square error (RMS), standard deviation (SD), and colorimetric surface discrepancy maps, enabling both quantitative and qualitative analysis of segmentation accuracy. Among the tested methods, the semi-automatic region growing approach demonstrated the highest agreement with manual segmentation (Dice = 0.935 ± 0.013; HD = 4.32 ± 0.48 mm), surpassing both other semi-automatic techniques and the automatic proprietary method. These results suggest that the region growing method implemented in 3D Slicer offers a reliable, accurate, and reproducible workflow for generating 3D liver models, particularly in surgical environments with limited access to advanced commercial solutions. The proposed methodology can potentially improve surgical planning, enhance training through realistic patient-specific models, and facilitate broader adoption of 3D printing in hepatobiliary surgery worldwide.
Co-Authors Edderbali, Fatima Essoukaki, Elmaati Farhan, Amr Harmouchi, Mohammed Labakoum, Badreddine Lyazidi, Aissam Mouhsen, Azeddine