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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 338 Documents
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Automated Detection and Grading of Tuberculosis Bacilli in Ziehl Neelsen-Stained Sputum Using YOLO with IUATLD-Based Classification Syevana Dita Musvika; Riries Rulaningtyas; Khusnul Ain; Pepy Dwi Endraswari; Annie Anak Joseph
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1111

Abstract

Tuberculosis (TB) remains one of the most pressing global health challenges, particularly in low- and middle-income countries, where diagnostic capacity is often limited. Accurate and efficient detection of Mycobacterium tuberculosis bacilli in sputum smear samples stained with Ziehl-Neelsen remains the cornerstone of TB diagnosis. However, conventional microscopic examination is inherently labor-intensive, subject to interobserver variability and prone to human error, leading to inconsistent diagnostic outcomes. Addressing these limitations, this study proposes the development of an automated bacilli detection and quantification system utilizing the YOLO (You Only Look Once) object detection framework, specifically the YOLOv8 architecture, to improve diagnostic accuracy, consistency, and efficiency in TB identification. The research methodology encompasses image acquisition of Ziehl Neelsen-stained sputum samples from the Microbiology Laboratory of Universitas Airlangga Hospital (RSUA) and publicly available repositories, followed by meticulous annotation using Roboflow. The annotated dataset was employed to train the YOLOv8 model, and performance was evaluated through key metrics, including accuracy, precision, and error rate. The developed model achieved an overall accuracy of 73.33%, with class-wise accuracies of 100% for BTA 1+, 80% for BTA 2+, and 40% for BTA 3+ categories, conforming to IUATLD classification standards. The suboptimal performance observed in the BTA 3+ category was attributed to discrepancies in Field of View (FOV) alignment between the microscope’s ocular lens and the attached digital camera, affecting image consistency. Despite this limitation, the results demonstrate the potential of YOLO-based automated detection systems to reduce dependence on manual analysis, enhance diagnostic objectivity, and accelerate TB screening workflows. Future work should prioritize hardware calibration, particularly FOV synchronization, and dataset diversification to further refine model performance and clinical applicability. The proposed approach represents a significant step towards scalable, rapid, and reliable TB diagnosis, with implications for broader adoption in resource-constrained healthcare environments.
Dynamic Fine-Tuning Strategy of Deep Learning Models for Lung Disease Classification on Chest X-ray Images Phuoc-Hai Huynh; Thi-Diem Truong
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1352

Abstract

Lung diseases remain a leading cause of life-threatening illnesses worldwide, particularly in developing countries with limited healthcare resources. In recent years, deep convolutional neural networks (CNNs) have demonstrated strong potential in the automatic interpretation of chest X-ray (CXR) images. However, existing approaches often rely on rigid two-stage fine-tuning or fixed-step progressive unfreezing strategies, which may fail to effectively adapt pretrained representations or destabilize optimization, especially when applied to imbalanced real-world datasets. This study proposes a validation-driven dynamic fine-tuning strategy for transfer learning that adaptively unfreezes network layers based on convergence signals observed on the validation set rather than predefined training epochs. By coupling the timing and depth of adaptation to generalization behavior, the proposed method enables controlled knowledge transfer while mitigating catastrophic forgetting and improving training stability. Experiments were conducted on a large-scale, real-world clinical dataset comprising 15,416 CXR images collected at An Giang Regional General Hospital, Vietnam. The proposed strategy was systematically evaluated across multiple CNN backbones, including Xception, DenseNet121, EfficientNetV2S, InceptionV3, MobileNet, ResNet50, and VGG16. Performance was assessed using overall accuracy and macro-F1 score to address class imbalance. Results demonstrate consistent improvements across all architectures, with a mean accuracy gain of 3.18% compared to conventional fine-tuning (p = 0.02). MobileNet achieved the best performance with 85.1% accuracy and 66.8% macro-F1, while maintaining a compact model size of 73.05 MB. These findings indicate that validation-driven dynamic fine-tuning provides a stable, statistically significant, and deployment-feasible transfer learning mechanism suitable for real-world clinical environments.
Cloud-Edge Collaborative Computing Framework for Stroke Disease Classification Using Machine Learning I Made Suartana; Ricky Eka Putra; Rahadian Bisma
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1702

Abstract

Stroke is the second leading cause of death and the third leading cause of disability worldwide. Artificial intelligence-based early detection in distributed environments faces three main obstacles: high latency in centralized cloud approaches, risks to patient data privacy during data transmission, and class imbalance in stroke datasets. This study proposes a three-layer collaborative computing framework, Cloud-Edge Collaborative Computing (CECC), which intelligently distributes the computational workload between edge nodes and the cloud for IoMT-based stroke risk classification. The primary novelty of this study lies in the hierarchical computing collaboration that enables real-time preprocessing at the edge layer, centralized model training at the cloud layer, and a local differential privacy mechanism (LDP, ε=0.5) that preserves patient data confidentiality during transmission, all entirely evaluated within a single unified multi-criterion benchmarking protocol. Gradient Boosting achieved the best performance in the hold-out evaluation with an accuracy of 95.01% and an AUC-ROC of 0.994. The CECC framework reduced inference latency by 44.9% (286.2ms to 157.8ms), bandwidth by 73.9% (3,240 to 847 Kbps), and memory by 84.4% (312.4 to 48.7 MB) with an accuracy degradation of only 0.30% compared to cloud only. This study is a simulation-based framework evaluation using a public retrospective dataset prospective clinical validation in a real IoMT environment remains necessary before actual clinical implementation because the dataset used is retrospective, small, highly imbalanced, and was not collected from a real IoMT system
Semantic-Filtered SMOTE-PSO for Breast Cancer Trial Eligibility Classification Taslim; Mumtazimah Mohamad
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1706

Abstract

This study addresses breast cancer clinical trial eligibility classification from free-text criteria under severe class imbalance, a condition that biases learning toward the majority class and complicates screening decisions when false positives and false negatives carry different operational costs. The study evaluates whether semantic plausibility control and optimization improve classification performance and screening-oriented error trade-offs under imbalanced conditions. The main contribution of this study is the proposed BEACoN framework, which integrates semantic-filtered augmentation and PSO-guided optimization within a unified screening-oriented eligibility classification setting. Four BioBERT-BiLSTM variants were evaluated using fixed train-validation-test partitions across three random seeds: a baseline model (M1), SMOTE augmentation (M2), SMOTE with cosine filtering (M2.5), and the proposed BEACoN framework (M3). Performance was evaluated using Precision, Recall, F1, AUROC, and AUPRC with pooled multi-seed statistical analysis to improve robustness and reduce single-seed bias. The evaluated augmentation-based configurations achieved pooled F1 scores up to 0.9381 ± 0.0005, AUROC up to 0.9976 ± 0.0001, and AUPRC up to 0.9808 ± 0.0004, indicating improved screening-oriented classification performance relative to the baseline. However, SMOTE with cosine filtering behaved broadly similarly to standard SMOTE under the evaluated embedding setting, indicating that the selected cosine threshold functioned largely as a permissive constraint, although modest seed-dependent prediction differences were still observed. Although BEACoN did not demonstrate statistically significant superiority over SMOTE in aggregate performance, it provided a more balanced false-positive and false-negative trade-off under comparable classification performance. Overall, the findings suggest that plausibility-controlled augmentation may provide practical value for screening-oriented eligibility classification under severe class imbalance
Wavelength Configuration and Signal Duration for Low-Complexity PPG-Based Anemia Detection: A Preliminary Validation Study Mulia Rahmah; Fatma Indriani; Rudy Herteno; Radityo Adi Nugroho; Irwan Budiman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1718

Abstract

Anemia remains a major global health problem, while standard diagnosis still depends on invasive hemoglobin testing, which may be less practical for repeated and resource-limited screening. Photoplethysmography (PPG) offers a potential non-invasive alternative, but the contribution of different wavelength configurations to anemia classification remains unclear. This preliminary subject-based validation study evaluated the effect of PPG wavelength configuration and recording duration on low-complexity anemia classification. A public dataset containing green, red, and infrared PPG recordings from 52 subjects was used, consisting of 42 normal and 10 anemia subjects. Eight morphological and temporal features were extracted from each wavelength. Seven signal configurations, namely Green, Red, IR, Green+Red, Green+IR, Red+IR, and all channels, were evaluated across 30, 45, 60, and 90 s recording durations. Support Vector Machine, Logistic Regression, Random Forest, and Extra Trees classifiers were trained using class-weighted learning and assessed with 5-fold subject-based cross-validation to reduce subject-level data leakage. The Red+IR configuration with a class-weighted SVM at 90 s achieved the best pooled performance, with a macro F1-score of 0.754, F1-Anemia of 0.588, anemia recall of 0.500, anemia precision of 0.714, accuracy of 0.769, and an error rate of 0.231. Fold-wise analysis showed substantial variability, with a macro F1-score of 0.617 ± 0.251, sensitivity of 0.467 ± 0.506, specificity of 0.846 ± 0.144, ROC-AUC of 0.864 ± 0.150, and PR-AUC of 0.694 ± 0.344. These findings suggest that adding more PPG wavelengths does not necessarily improve classification performance. However, the model still missed 5 of 10 anemia cases, and the limited anemia recall, small minority class, and demographic imbalance indicate that the results should be interpreted as preliminary and require validation on larger, more balanced datasets.
Three-Arm Robotic Diagnostic Coordination Using Artificial Neural Network-Based Decision Support Hariprasath Manoharan; Murugesh T.S; Abirami Manoharan; Durga R; Senthilkumar M
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1752

Abstract

The growing demand for smart healthcare systems and increasing burden on healthcare professionals have necessitated the need for autonomous diagnostic technologies that can facilitate real-time clinical decision-making. Current robotic diagnostic systems are often limited to discrete tasks, including sensing, monitoring, and diagnostic support. This results in limited coordination, transparency, and decision-making capabilities. The aim of the proposed method is to design a three-arm diagnostic robot with Artificial Neural Network (ANN) intelligence to improve healthcare support. The proposed framework includes dedicated robotic arms for sensing, visualization, and diagnostic tool manipulation, along with a coordinated communication architecture. A decision-support module based on an ANN gathers diagnostic information from the different subsystems of a robot and offers intelligent diagnostic evaluations. A seven-axis coordination approach is implemented to improve the synchronous performance of robotic components and to reduce the operational liabilities during diagnostic operations. The proposed framework was evaluated with four scenarios, and the performance was assessed in terms of transparency, coordination efficiency, association error, diagnostic accuracy, sensing latency, and communication delay. The experimental results showed that the proposed system achieved a diagnosis accuracy of 93% versus 71% for the baseline method. Moreover, the framework achieved 93% of transparency rate, 85% of coordination efficiency, 12% of reduction of association error, a 40 ms sensing latency, and a 15 ms communication delay. Statistical analysis reported consistent performance with deviation values of 1.2%, 1.7%, and 1.3% for arm coordination, visualization, and diagnostic tool management, respectively. The results confirm that the combination of ANN-based decision support and synchronized multi-arm robotic work can significantly improve the diagnostic efficiency and the operational reliability. The proposed architecture provides a strong foundation for future intelligent healthcare systems and enables the development of autonomous robotic diagnostics for advanced medical applications
Accuracy Enhancement of a Hybrid CNN–VGG16 Architecture through Dropout Regularization Strategy for Breast Cancer Histopathology Classification Fawaidul Badri Fawaid; Ilham Ari Elbaith Zaeni; Heru Wahyu Herwanto Heru; Muhammad Khusairi Osman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1356

Abstract

Breast cancer remains the leading cause of cancer-related mortality among women globally, necessitating accurate diagnosis through histopathological image analysis. However, manual examination of these images is time-consuming and susceptible to inter-observer variability, highlighting the critical need for reliable automated computer-aided diagnostic (CAD) systems. This study was conducted to systematically evaluate and optimize convolutional neural network (CNN) architectures for automated classification of breast cancer histopathology images, with a focus on mitigating overfitting and enhancing diagnostic accuracy through hybrid deep learning methodologies. The principal innovation is the development of a CNN-VGG16 hybrid architecture that strategically integrates pre-trained feature extraction with a customized CNN framework, hypothesized to substantially improve classification accuracy and model generalization. Three model configurations were developed and comparatively analyzed: (1) baseline CNN, (2) CNN with dropout regularization, and (3) hybrid CNN-VGG16 model. Input images underwent preprocessing, including resizing to 150×150 pixels, normalization, and data augmentation. All models were trained with identical hyperparameters: an Adam optimizer with a learning rate of 0.001, a batch size of 32, and 10 epochs. Dropout regularization with a fixed rate of 0.5 was applied to fully-connected layers to mitigate overfitting. Model evaluation was conducted utilizing standard performance metrics. The proposed CNN-VGG16 hybrid model achieved superior performance: accuracy of 85.19%, precision of 87.16%, recall of 92.75%, and F1-score of 88.37%. These metrics represent significant improvements of 4.2% relative to baseline CNN and 3.4% compared to the dropout-regularized variant, indicating substantially enhanced diagnostic capability and reduced false-negative rates. Strategic integration of pre-trained feature extraction with customizable CNN architectures significantly improves generalization and classification performance in histopathological image analysis. Future investigations should incorporate larger heterogeneous datasets, attention mechanisms, and explainable artificial intelligence (XAI) to enhance clinical interpretability and strengthen practitioner confidence in digital pathology systems
BackMix-Enhanced Semi-Supervised Learning for Automated Detection of Aortic Stenosis from Transthoracic Echocardiographic Images Fatima Ezzahra Elkouahy; Badreddine Labakoum; Hajar Ouahid; Mansoor Alam; Hamid El Malali; Lhoucine Ben Taleb; Azeddine Mouhsen
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1657

Abstract

Aortic stenosis (AS) is a prevalent and progressive valvular heart disease requiring accurate severity assessment for optimal clinical decision-making. Transthoracic echocardiography (TTE) is the standard diagnostic modality; however, its interpretation remains operator-dependent and subject to inter-observer variability. In this study, we propose an anatomically guided BackMix-enhanced semi-supervised learning framework for automated echocardiographic view classification and AS severity assessment. The approach leverages Gradient-weighted Class Activation Mapping (Grad-CAM) to preserve diagnostically relevant anatomical regions during augmentation while modifying background areas to mitigate shortcut learning. A semi-supervised self-training strategy combined with an ensemble classification framework was used to exploit both labeled and unlabeled data. The framework was evaluated on the TMED2 dataset of echocardiography, comprising 24,964 TTE images. To assess generalizability, external validation was performed on an independent dataset of 300 cardiologist-labeled echocardiographic images, including apical four-chamber (A4C), parasternal long-axis (PLAX), and parasternal short-axis (PSAX) views. Experimental results demonstrated that the proposed method outperformed the baseline semi-supervised model, improving view classification accuracy by approximately 3% and AS severity classification accuracy by 4–6%, with the largest gain observed in moderate AS. Performance remained consistent on the external validation dataset, supporting the robustness of the proposed approach. Statistical analysis confirmed the significance of these improvements (p < 0.01). Grad-CAM evaluation further demonstrated improved localization of clinically relevant regions. These findings suggest that anatomically guided BackMix augmentation combined with semi-supervised ensemble learning can improve classification accuracy, robustness, and interpretability in echocardiographic analysis under limited annotation conditions, offering a promising approach for automated AS assessment across independent clinical datasets
Adaptive Attention-Driven ALEXIS Framework for High-Precision ECG Signal Classification Nandakumar P; Kotteeswaran C; Sathya S; Kavitha P; Edwin Raja S; Selvaraj D
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1716

Abstract

Accurate classification of electrocardiogram (ECG) signals is essential for the early identification of cardiac abnormalities and for supporting timely clinical decision-making. However, conventional machine learning and deep learning approaches often assume stationary signal characteristics and therefore struggle to model the complex non-linear, non-stationary, and patient-specific nature of ECG signals. These limitations reduce their ability to capture subtle morphological variations and temporal dependencies associated with abnormal cardiac rhythms. To address these challenges, this paper proposes an Adaptive Attention-Driven ALEXIS Framework, a hybrid deep learning architecture that integrates multiscale signal decomposition, hierarchical feature learning, and adaptive attention-guided fusion for five-class ECG abnormality classification. The proposed framework combines the Stockwell Transform (ST) for time-frequency analysis, Empirical Mode Decomposition (EMD) for adaptive extraction of intrinsic oscillatory modes, and Local Phase Quantization (LPQ) for robust morphological feature representation. The extracted multiscale features are processed through an AlexNet-based spatial learning module and an LSTM-based temporal modeling module, followed by an attention-guided feature fusion mechanism and Support Vector Machine (SVM) classifier for robust decision making. The framework was evaluated using the MIT-BIH Arrhythmia Database comprising 109,494 annotated ECG beats, which were partitioned into 80% training (approximately 87,595 beats) and 20% testing (approximately 21,899 beats) using a stratified beat-level split. Experimental evaluation on the independent test set achieved an overall accuracy of 98.43%, sensitivity of 98.27%, specificity of 98.78%, and F1-score of 98.31%. These results demonstrate that the proposed ALEXIS framework effectively captures complementary spectral, temporal, and morphological ECG characteristics, providing accurate and interpretable ECG abnormality classification while establishing a strong foundation for future validation using inter-patient evaluation protocols and cross-database clinical studies
Non-Contact Multispectral Image Binary Classification of Water and Sodium Hydroxide Solutions Using Convolutional Neural Networks Siti Rusdiana; Asep Rusyana; Juwita; Mauliza Putri; Aufa Rafiki; Souvik Das
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1727

Abstract

Identification of visually transparent liquids remains a challenging problem in non-contact sensing because chemically different solutions can appear nearly identical under normal observation. In laboratory and industrial environments, direct-contact chemical measurements are reliable but may require sample handling, probe calibration, cleaning, and additional processing time, which can limit their use in rapid or automated monitoring systems. This study aims to develop and evaluate a non-contact image-based classification framework for distinguishing pure water (H₂O) from sodium hydroxide solution (H₂O with NaOH) using multispectral fluctuation-pattern images. The proposed approach integrates image preprocessing, K-means segmentation, and a convolutional neural network (CNN)-based classification. A balanced dataset of 1,050 multispectral images, consisting of 525 images for each class, was used in the experiment. Each image was resized, converted to grayscale, normalized, and segmented using K-means clustering to emphasize the dominant liquid-region fluctuation pattern before classification. Three CNN architectures, namely InceptionV3, VGG19, and DenseNet201, were trained and compared under identical data-splitting and evaluation conditions. The experimental results showed that VGG19 achieved the best testing performance, with an accuracy of 97.47%, precision of 95.18%, recall of 100.00%, and F1-score of 97.53%. DenseNet201 obtained 94.30% accuracy, while InceptionV3 achieved 89.24% accuracy. These results indicate that multispectral fluctuation-pattern images contain discriminative optical information that can be learned effectively by CNN models, even when the liquid samples are visually indistinguishable to the human eye. The proposed framework demonstrates the feasibility of non-contact transparent liquid identification and may support the development of automated monitoring systems for laboratory, chemical, and industrial applications where direct sample contact is undesirable or impractical.

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