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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Educational Insights and Innovations (IJEDINS) International Journal of Technology and Modeling
Patel, Priya
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Explainable Artificial Intelligence based Deep Learning for Retinal Disease Detection Sureja, Nitesh; Parikh, Vruti; Rathod, Ajaysinh; Patel, Priya; Patel, Hemant; Sureja, Heli
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.v7i2.717

Abstract

This research focuses on the automated identification of retinal diseases. To address this challenge, an artificial intelligence-based approach developed utilizing five deep learning models namely Xception, InceptionV4, EfficientNet-B4, SqueezeNet, and ResNet-264. The model leverages transfer learning to enhance its performance. It is trained on a dataset of optical coherence tomography (OCT) images to classify retinal conditions into four categories: (1) diabetic macular edema, (2) choroidal neovascularization, (3) drusen, and (4) normal. The training dataset, sourced from publicly available repositories, comprises 1,08,312 OCT retinal images covering all four categories. The proposed models achieved good results. InceptionV4 outperformed other models across multiple metrics, achieving the highest accuracy (99.50%), precision (100%), recall (100%), AUC (100%), and F1 score (100%). It surpassed SqueezeNet (accuracy: 98.00%, precision: 98.00%, recall: 98.00%), EfficientNet-B4 (accuracy: 98.50%, precision: 98.50%, recall: 98.50%), Xception (accuracy: 78.25%, precision: 80.36%, recall: 77.75%, F1 score: 99.50%), and ResNet-264 (accuracy: 87.75%, precision: 87.94%, recall: 87.50%, F1 score: 87.98%). The results highlight the effectiveness of deep learning models combined with transfer learning in achieving accurate and efficient retinal disease detection. Future research could focus on expanding the dataset and exploring hybrid architectures to enhance classification accuracy and improve generalization across various retinal conditions
Exploring the Integration of Augmented Reality in Programming Education to Enhance Student Engagement in High Schools Sharmar, Rahul; Patel, Priya
International Journal of Educational Insights and Innovations Vol. 1 No. 2 (2024): December 2024 - International Journal of Educational Insights and Innovations (
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The integration of Augmented Reality (AR) in programming education has emerged as a promising approach to enhance student engagement and learning outcomes, particularly in high school settings. This study investigates the effectiveness of AR-based learning tools in improving students' understanding of programming concepts and fostering critical thinking skills. Using a mixed-methods approach, the research involved 60 high school students divided into an experimental group (using AR tools) and a control group (following traditional methods). Data was collected through surveys, classroom observations, performance assessments, and qualitative interviews. Results indicate that the experimental group demonstrated significantly higher levels of engagement, with 85% reporting increased interest in programming compared to 60% in the control group. Additionally, the experimental group outperformed the control group in both theoretical knowledge and practical application, scoring 25% higher on quizzes and completing tasks 20% faster. Qualitative feedback highlighted the immersive and interactive nature of AR as key factors in enhancing learning experiences. However, challenges such as technical difficulties and the need for teacher training were also identified. The findings suggest that AR can effectively bridge the gap between theoretical knowledge and practical application, making it a valuable tool for programming education. This study provides insights for educators and policymakers on integrating AR into curricula and highlights the importance of addressing technical and training barriers to ensure successful implementation.
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Co-Authors Iyer, Ananya Joshi, Nikhil Kumar, Rohan Mehta, Rahul Parikh, Vruti Patel, Hemant Rathod, Ajaysinh Sharma, Arjun Sharmar, Rahul Sureja, Heli Sureja, Nitesh