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Kelapati, Kelapati
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Diabetes detection and prediction through a multimodal artificial intelligence framework Kulkarni, Gururaj N.; Kelapati, Kelapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp459-468

Abstract

Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.
Advancements and challenges in deep learning techniques for lung disease diagnosis Bagalkot, Laxmi; Kelapati, Kelapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1053-1062

Abstract

This study explores the application of deep learning (DL) techniques in diagnosing lung diseases using screening methods such as Chest X-Rays (CXRs) and computed-tomography (CT) scans. The motivation for this research stems from the need for advanced diagnostic tools in healthcare, with DL showing significant potential in medical image analysis. Despite advancements, challenges such as high costs of CT scans, processing time constraints, image noise, and variability persist. To address these issues, the study conducts a thorough literature survey to identify diverse preprocessing techniques, detection algorithms, and classification models designed for CXR analysis. In conclusion, this work contributes to the advancement of medical imaging technologies by offering innovative solutions, acknowledging existing limitations, and addressing the challenges in lung disease diagnosis. Future research should focus on further refining these techniques and exploring their application in broader clinical settings.
Co-Authors Bagalkot, Laxmi Kulkarni, Gururaj N.