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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 24 Documents
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Search results for , issue "Vol 7 No 4 (2025): October" : 24 Documents clear
Precise Electrocardiogram Signal Analysis Using ResNet, DenseNet, and XceptionNet Models in Autistic Children Yunidar, Yunidar; Melinda, Melinda; Albahri, Albahri; Ramadhani, Hanum Aulia; Dimiati, Herlina; Basir, Nurlida
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.v7i4.1044

Abstract

In autistic children, one of the important physiological aspects to be examined is the heart condition, which can be assessed through electrocardiogram (ECG) signal analysis. However, ECG signals in autistic children often contain interference in the form of noise, making the analysis process, both manual and conventional, challenging. Therefore, this study aims to analyze the ECG signals of autistic children using a classification method to distinguish between two main conditions: playing and calm conditions. A deep learning approach employing the Convolutional Neural Network (CNN) architectures was used to obtain accurate results in distinguishing the heart conditions of autistic children. The data used consists of 700 ECG signal data in each class, processed through the filtering, windowing, and augmentation stages to obtain balanced data. Three CNN architectures, ResNet, DenseNet, and XceptionNet, were tested in this study. Although these architectures are originally designed for 2D and 3D image data, modifications were made to adapt the input data structure to perform 1D data calculations. The evaluation results show that the XceptionNet model achieved the best performance, with accuracy, precision, recall, and F1-score of 97,14% each, indicating a good ability in capturing the complex patterns of ECG signals. Meanwhile, the ResNet obtained good results with 96,19% accuracy, while DenseNet performed slightly lower results with 94,76% accuracy and evaluation metrics. Overall, this study demonstrates that a deep CNN architecture based on dense connections can enhance the accuracy of ECG signal classification in autistic children.
Adaptive Threshold-Enhanced Deep Segmentation of Acute Intracranial Hemorrhage and its Subtypes in Brain CT Images Suganthi, R.; Yalagi, Pratibha C. Kaladeep; Chowdhury, Rini; Kumar, Prashant; Sharmila, D.; Krishna, Kunchanapalli Rama
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.v7i4.1048

Abstract

Accurate segmentation of acute intracranial haemorrhage (ICH) in brain computed tomography (CT) scans is crucial for timely diagnosis and effective treatment planning. While the RSNA Intracranial Hemorrhage Detection dataset provides a substantial amount of labeled CT data, most prior research has focused on slice-level classification rather than precise pixel-level segmentation. To address this limitation, a novel segmentation pipeline is proposed that combines a 2.5D U-Net architecture with a dynamic adaptive thresholding technique for enhanced delineation of hemorrhagic lesions and their subtypes. The 2.5D U-Net model leverages spatial continuity across adjacent slices to generate initial lesion probability maps, which are subsequently refined using an adaptive thresholding method that adjusts based on local pixel intensity histograms and edge gradients. Unlike fixed global thresholding approaches such as Otsu’s method, the proposed technique dynamically varies thresholds, enabling more accurate differentiation between hemorrhagic tissue and surrounding brain structures, especially in challenging cases with diffuse or overlapping boundaries. The model was evaluated on carefully selected subsets of the RSNA dataset, achieving a mean Dice similarity coefficient of 0.82 across all ICH subtypes. Compared to standard U-Net and DeepLabV3+ architectures, the hybrid approach demonstrated superior accuracy, boundary precision, and fewer false positives. Visual analysis confirmed more precise lesion delineation and better correspondence with manual annotations, particularly in low-contrast or complex anatomical regions. This integrated approach proves effective for robust segmentation in clinical environments. It holds promise for deployment in computer-aided diagnosis systems, providing radiologists and neurosurgeons with a reliable tool for comprehensive ICH assessment and enhanced decision-making during emergency care
Vision Language Transformer Framework for Efficient Cancer Diagnosis through Multimodal Integration Gutam, Bala Gangadhara; Malchi, Sunil Kumar
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.v7i4.1075

Abstract

Finding and treating cancer as soon as possible help patients get better outcomes. Patients requiring imaging or biopsy tests sometimes find it challenging to access them because these procedures are often limited by their high cost and availability in clinical settings. Recent AI methods, particularly those involving deep learning, can address these problems and significantly enhance the process for detecting cancer, offering greater efficiency and scalability. In this context, LLMs and VLMs are considered leading solutions for trying to make sense of multimodal variables within AI-driven healthcare systems. Although LLMs are strong at working with unstructured, clinically related text data, they have not often been used for patient assessment beyond descriptive or summarization tasks, by combining images and descriptions, along with both structured and unstructured data. The VLMs allow doctors and medical researchers to catch cancer symptoms from multiple angles. In this work, we study both LLMs and VLMs in cancer detection, analyzing their architectures, learning mechanisms, and performance on various datasets, and identifying directions for expanding multimodal AI in healthcare. Our results indicate that combining these two data types enhances how accurately we are able to diagnose patients across different types of cancer. Our studies in MIMIC-III, MIMIC-IV, TCGA, and CAMELYON 16/17 datasets revealed that multimodal transformer models significantly improve the accuracy of diagnosing biopsy results. In particular, BioViL achieves an AUC-ROC of 0.92 for detecting lung cancer, whereas CLIP Fine-tuned achieves a similar result of 0.91 for colon cancer detection.
Breast Cancer Classification Using z-score Thresholding and Machine Learning Yildirim, Mustafa Eren; Salman, Yucel B.
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.v7i4.1165

Abstract

Image processing and machine learning are being used in biomedical applications as supporting tools for the detection and diagnosis of certain diseases. Breast cancer is one of these diseases that researchers have devoted great effort to for decades. To accomplish this task, image-based and feature-based public datasets are available for use. Due to several factors such as hardware limitations or preprocessing, images can become noisy. The noise in images, which can lead to anomalies or outliers in the dataset, may decrease detection accuracy and mislead medical staff during the diagnostic stage. Therefore, this study aims to present the effect of removing outliers from the dataset on the detection accuracy of breast cancer. The proposed method removes outliers detected through z-score analysis. The remaining data are normalized, and the classification accuracies of ten methods are obtained through direct implementation. The methods include XGBoost, Neural Network, CNN, RNN, AdaBoost, LSTM, GRU, Random Forest, SVM, and Logistic Regression. The public dataset Wisconsin Diagnostic Breast Cancer (WDBC) was used in this study. An ablation study was conducted by fine-tuning the threshold value of the z-score method. The results showed that the best accuracy was obtained when the threshold value was set to 3. Additionally, a comparison was made between the results obtained using the entire dataset and the dataset after outlier removal. The results showed that the average accuracy of all classifiers was 98.08%. In conclusion, the findings indicate that removing outliers from the dataset increases the overall accuracy of breast cancer detection

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