A Comparative Analysis of Deep Learning Models in Diverse AI Healthcare Applications

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : S. Hemalatha, Kiran Mayee Adavala, S.N. Chandra Shekhar, Pullela SVVSR Kumar, A.R. Venkataramanan, D. Naga Malleswari

10.14445/23488549/IJECE-V12I3P111

How to Cite?

S. Hemalatha, Kiran Mayee Adavala, S.N. Chandra Shekhar, Pullela SVVSR Kumar, A.R. Venkataramanan, D. Naga Malleswari, "A Comparative Analysis of Deep Learning Models in Diverse AI Healthcare Applications," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 3, pp. 116-125, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I3P111

Abstract:

This review article made the comparative analysis quantitatively evaluates four applications of deep learning in healthcare: tuberculosis detection in chest radiography, skin cancer classification, Electronic Health Record (EHR) analysis, and drug discovery. The studies exhibit distinct model architectures and metrics, with tuberculosis and skin cancer classification achieving AUC values of 0.99 and above 0.91, respectively, using CNNs optimized with transfer learning and data augmentation. EHR analysis, utilizing CNN and RNN hybrids, reports AUCs between 0.70 and 0.85 for tasks like disease progression and patient readmission, demonstrating the variability introduced by heterogeneous, sequential data. Drug discovery models employ RNNs for molecular sequence prediction, highlighting a conceptual framework rather than specific performance metrics. The findings indicate that image-based models excel in quantitative performance and scalability, while models for EHR and molecular data face challenges in standardization and interpretability. This analysis underscores the need for data harmonization and explainability to enhance the clinical readiness of deep learning across diverse healthcare domains.

Keywords:

Deep Learning, Healthcare, Tuberculosis detection, Skin cancer classification, Electronic Health Records (EHR), CNNs, RNNs, Artificial Intelligence (AI).

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