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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Raja Tariqul Hasan Tusher
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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On body e-shaped patch antenna for biomedical application Raja Rashidul Hasan; Raja Tariqul Hasan Tusher; Sujan Howlader; Sharmin Jahan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.828 KB) | DOI: 10.52549/ijeei.v7i1.516

Abstract

An E-shaped micro strip patch antenna is designed and analyzed in this paper which operates in MICS (402.0-406.0MHz) band. The Performance has been observed on a body of human phantom model as well as in free space with different conducting material for the designed antenna. The height of this antenna is taken 1.5mm from the ground plane. At resonance frequency of 405 MHz the S11 parameter is obtained in free space is -23.26dB for conducting material of aluminum and -17.96dB is measured on human phantom body at 405 MHz of resonance frequency. FR4 is used as substrate material. The Specific Absorption Rate (SAR) is found to be 0.3562 W/kg by placing the antanna on human phantom model. VSWR and directivity has been analyzed also.
Deep neural networks for predicting kidney health: focus on cyst, stone, tumor, and normal classification Abdey Rabby; Jannatun Naima Jannat; Md Assaduzzaman; Rahmatul Kabir Rasel Sarker; Raja Tariqul Hasan Tusher
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10084

Abstract

Kidney diseases affect individuals across all age groups and are a major global health concern. Pathological and other conditions, such as tumors, cysts, and stones, along with normal states of the kidneys, need to be detected as early as possible to improve treatment outcomes and quality patient care. This study looks into the use of computed tomography (CT) images for deep learning-based kidney disease classification. We evaluated four widely used convolutional neural networks (CNNs) such as VGG16, MobileNetV2, ResNet50, and InceptionV3 on a dataset of 12,456 CT images. Among the individual models, MobileNetV2 achieved the highest validation accuracy of 99.64%. As a novel contribution, we propose a hybrid deep learning model that combines MobileNetV2 and ResNet50 to enhance diagnostic performance. The hybrid architecture design led to superior results: 99.88% validation accuracy, 99.50% precision, 99.50% recall, 99.25% F1-score, and a reduced validation loss of 0.0090. Performance was further validated using confusion matrices, receiver operating characteristic (ROC) curves, classification reports, and 6-fold cross-validation to assess generalization. The proposed model demonstrates strong robustness and generalizability across kidney condition categories. As far as we are aware, not many research have looked into a hybrid combination of MobileNetV2 and ResNet50 for multi-class kidney CT classification.
Co-Authors Abdey Rabby Jannatun Naima Jannat Md Assaduzzaman Rahmatul Kabir Rasel Sarker Raja Rashidul Hasan Sharmin Jahan Sujan Howlader