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All Journal Journal of Applied Pharmaceutical Research Journal of Electronics, Electromedical Engineering, and Medical Informatics
Prashant Kumar
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Medicine & Pharmacology

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Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

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Deep Learning based classification of ECG signals using RNN and LSTM Mechanism V, Satheeswaran; G.Naga Chandrika; Ankita Mitra; Rini Chowdhury; Prashant Kumar; Glory E
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.496

Abstract

The Electrocardiogram (ECG) stands as a pivotal tool in cardiovascular disease diagnosis, widely embraced within clinical domains for its simplicity and effectiveness. This paper presents a novel method for classifying ECG signals by leveraging deep learning techniques, specifically Long Short-Term Memory (LSTM) networks enhanced with an attention mechanism. ECG signals encapsulate vital insights into cardiac activities and abnormalities, underscoring the importance of precise classification for diagnosing heart conditions. Conventional methods often confront with the intricate variability of ECG signals, prompting the exploration of sophisticated machine learning models. Within this framework, an attention mechanism is seamlessly integrated into the LSTM architecture, dynamically assigning significance to different segments of the input sequence. This adaptive mechanism permits the model to focus on relevant features for classification, thereby bolstering interpretability and performance by highlighting crucial aspects within the ECG signals. Experiments conducted on the MIT/BIH dataset have yielded compelling findings, boasting an impressive overall classification accuracy of 98.9%. Precision stands at 0.993, recall at 0.992, and the F1 score at 0.99, underscoring the robustness of the results. These findings underscore the potential of the proposed methodology in significantly enhancing ECG signal analysis, thereby facilitating more accurate diagnosis and treatment decisions in the realm of cardiac healthcare.
Ensemble learning based Convolutional Neural Network – Depth Fire for detecting COVID-19 in Chest X-Ray images Chandrika, G Naga; Chowdhury, Rini; Prashant Kumar; K, Sangamithrai; E, Glory; M D, Saranya
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.525

Abstract

The Unique Corona virus-caused COVID19 deadly disease has gave out a significant dispute to healthcare systems around the world. To stop the virus's transmission and lessen its negative effects on public health, it is crucial to recognise correctly and rapidly those who have COVID19. The application of artificial intelligence (AI) holds the capacity to increase the precision and effectiveness of COVID19 diagnosis. The purpose of the study is to build a reliable AI-based model capable correctly detect COVID19 cases from chest X-ray pictures. A dataset of 16,000 chest X-ray pictures, including COVID19 positive and negative instances, is employed in the investigation. Four convolutional neural network (CNN) the models that previously been trained are employed in the proposed model, and the output of each model is combined using an ensembling technique. The major objective of this project is to develop an accurate and reliable AI-based model to classify COVID19 cases from chest X-ray images. The individuality of this method comes in its capacity to employ data augmentation strategies to enhance model generalisation and prevent overfitting. The accuracy and dependability of the model are moreover advanced by utilising numerous pre-trained CNN models and ensembling methods. The suggested AI-based model's classification accuracy for the five classes (bacterial, COVID19 positive, negative, opacity, and viral), the three classes (COVID19 positive, negative, and healthy), and the two classes (COVID19 positive and negative) was 97.3%, 98.2%, 97.6%, and respectively. The projected model performs better in terms of sensitivity, accuracy and specificity than unconventional techniques that are previously in use. Significant ability may be guided in the suggested AI-based model's ability to recognize COVID19 cases quickly and effectively from X-rays of the chest. This approach can help radiology physicians analyse affected role quickly and correctly, improving patient outcomes and lessening the strain on healthcare systems. To ensure the precision of the diagnosis, it is vital to mention that the model's decisions should be made in consultation with a licenced medical expert.
Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things D, Shamia; M, Ephin; Yalagi, Pratibha C. Kaladeep; Chowdhury , Rini; Prashant Kumar; R, Prabhu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.611

Abstract

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.
Hybrid Fuzzy Logic and Metaheuristic Optimized Trinetfusion Model for Liver Tumor Segmentation Mohammed Ashik; Patrick, Arun; D. Dennis Ebenezer; Rini Chowdhury; Prashant Kumar; Ida, S. Jhansi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.657

Abstract

Liver tumor segmentation plays a vital role in medical imaging, enabling accurate diagnosis and precise treatment planning for liver cancer. Traditional methods such as threshold-based techniques and region-growing algorithms have been explored, and more recently, deep learning models have shown promise in automating and improving segmentation tasks. However, these approaches often face significant limitations, including challenges in accurately delineating tumor boundaries, high sensitivity to noise, and the risk of overfitting, especially when dealing with complex tumor structures and limited annotated data. To overcome these limitations, a novel Hybrid Fuzzy Logic and Metaheuristic Optimized TriNetFusion Model is proposed. This model integrates the strengths of fuzzy logic, metaheuristic optimization, and deep learning to deliver a more reliable and adaptable segmentation framework. Fuzzy logic is utilized to handle the inherent uncertainty and ambiguity in medical images, particularly in tumor boundary regions where intensity variations are subtle and complex. Metaheuristic optimization algorithms are employed to fine-tune the parameters of the segmentation model effectively, ensuring a more generalized and adaptive performance across different datasets. At the core of the model lies TriNetFusion, a multi-branch deep learning architecture that fuses complementary features extracted at various levels. The fusion of these multi-level features contributes to robust segmentation by capturing both global and local image characteristics. This model is specifically designed to adapt to irregular and complex tumor shapes, significantly reducing false positives and improving boundary precision. Experimental validation using benchmark liver tumor datasets demonstrates that the proposed model achieves a segmentation accuracy of 96% with a low loss value of 0.2, indicating strong generalization without overfitting. The hybrid approach not only enhances segmentation precision but also ensures robustness and adaptability, making it a highly promising solution for liver tumor segmentation in clinical practice.
Enhancing Skin Cancer Classification with Mixup Data Augmentation and Efficientnet D, Shamia; Umapriya, R.; Prasad, M. L. M.; Rini Chowdhury; Prashant Kumar; K.Vishnupriya
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.703

Abstract

Skin lesion classification and segmentation are two crucial tasks in dermatological diagnosis, here automated approaches can significantly aid in early detection and improve treatment planning. The proposed work presents a comprehensive framework that integrates K-means clustering for segmentation, Mixup augmentation for data enhancement, and the EfficientNet B7 model for classification. Initially, K-means clustering is applied as a pre-processing step to accurately segment the lesion regions from the background, ensuring that the model focuses on processing the most relevant and informative features. This segmentation enhances the model’s ability to differentiate between subtle lesion boundaries and surrounding skin textures. To address the common issue of class imbalance and to improve the overall robustness of the classification model, Mixup augmentation is employed. This technique generates synthetic samples by linearly interpolating between pairs of images and their corresponding labels, effectively enriching the training dataset and promoting better generalization. For the classification task, EfficientNet B7 is utilized due to its superior feature extraction capabilities, optimized scalability, and excellent performance across various image recognition challenges. The entire pipeline was evaluated on a dataset comprising 10,015 dermatoscopic images covering seven distinct categories of skin lesions. The proposed method achieved outstanding performance, demonstrating a precision rate of 95.3% and maintaining a low loss of 0.2 during evaluation. Compared to traditional machine learning and earlier deep learning approaches, the proposed framework showed significant improvements, particularly in handling complex patterns and imbalanced datasets, making it a promising solution for real-world clinical deployment in dermatology.
Co-Authors Abhishek Singh Ankita Mitra Anurag Verma Chandrika, G Naga Chowdhury , Rini Chowdhury, Rini D, Shamia D. Dennis Ebenezer E, Glory G.Naga Chandrika Glory E Ida, S. Jhansi K, Sangamithrai K.Vishnupriya M D, Saranya M, Ephin Mohammed Ashik Patrick, Arun Prasad, M. L. M. R, Prabhu Rini Chowdhury Umapriya, R. V, Satheeswaran Yalagi, Pratibha C. Kaladeep