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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 292 Documents
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Optimized Recurrent Neural Network Based on Improved Bacterial Colony Optimization for Predicting Osteoporosis Diseases B, Sivasakthi; K, Preetha; D, Selvanayagi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1410

Abstract

Osteoporosis is a silent disease before significant fragility fractures despite its high prevalence, and its screening rate is low. In predictive healthcare analytics, the Elman recurrent neural network (ERNN) has been widely used as a learning technique. Traditional learning algorithms have some limitations, such as slow convergence rates and local minima that prevent gradient descent from finding the global minimum of the error function. The main goal is to precisely estimate each individual's risk of developing osteoporosis. These forecasts are essential for prompt diagnosis and treatment, which have a significant influence on patient outcomes. Hence, the present research focuses on making a more efficient prediction method based on an optimized Elman recurrent neural network (ERNN) for predicting osteoporosis diseases. An optimized ERNN method, IBCO-ERNN, improved bacterial colony optimization (IBCO) by optimizing the ERNN weights and biases. The IBCO approach uses an iterative local search (ILS) algorithm to enhance convergence rate and avoid the local optima problem of conventional BCO. Subsequently, the IBCO is used to optimize the ERNN's weights and biases, thereby improving convergence speed and detection rate. The effectiveness of IBCO-ERNN is evaluated using four different types of osteoporosis datasets: Femoral neck, Lumbar spine, Femoral and Spine, and BMD datasets. The proposed IBCO-ERNN produced higher accuracy at 95.61%, 96.26%, 97.26%, and 97.54 % for the Femoral neck, Lumbar spine, Femoral, and Spine datasets, respectively. The experimental findings demonstrated that, compared with other predictors, the proposed IBCO-ERNN achieved respectable accuracy and rapid convergence.
Impact of Optimizer Algorithm on NasNetMobile Model for Eight-class Retinal Disease Classification from OCT Images Selvarajan, Madhumithaa; M, Masoodhu Banu N.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1464

Abstract

Artificial intelligence (AI) is an emerging technology that plays a vital role in various fields, including the medical field. Ophthalmology is the earliest field to adopt AI for diagnosing several retinal diseases. Many imaging techniques are available, but Optical Coherence Tomography (OCT) is particularly useful for early-stage diagnosis. OCT is a non-invasive imaging method that offers high-resolution visualization of the retinal structure, aiding the ophthalmologist in differentiating between normal and abnormal retina. Automated OCT-based retinal disease classification using deep learning (DL) is important for early disease detection. Most DL models achieved high performance, but the influence of the optimizer on model behaviour, convergence, and explainability remains a challenge. To bridge the gap, this study evaluates the performance and convergence of five optimizers, such as RMSprop, AdamW, Adam, Nadam, and SGD, on the NasNetMobile model. The model was trained on the OCT-8 dataset, which comprises seven diseased retinal classes and one normal class of Optical Coherence Tomography (OCT) images. The seven diseases are Age-related Macular Degeneration (AMD), choroidal neovascularization (CNV), Central Serous retinopathy (CSR), diabetic macular edema (DME), diabetic retinopathy (DR), DRUSEN, and Macular Hole (MH). The study also analyzes convergence behaviour and explainability through early stopping regularization technique and GradCAM XAI, respectively. The model achieved 71%, 93%, 96%, 97%, and 97% of accuracy, respectively. Compared with other optimizers, the SGD optimizer achieved high accuracy in 22 epochs, which indicates better generalization. GradCAM XAI highlights the disease-relevant region across different retinal diseases. This framework emphasizes the significance of selecting an appropriate optimizer for robust retinal disease classification using a DL model trained on OCT images

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