STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Health Professions Review Article

Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction

  1. Rajeshwaran V Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore
  2. Logeshwaran N Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore
  3. Manjubhasini R Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore
  4. Keerthika Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore

Abstract

Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the overall performance and robustness of the model. Optimization ensures that the computational resources are effectively utilized, leading to improved convergence rates and reduced training time. Transfer learning leverages pre-trained models on large benchmark datasets, allowing the framework to extract high-level feature representations and achieve superior accuracy even with limited domain-specific data. Data augmentation further strengthens the system by artificially increasing the diversity of the training dataset, helping the model generalize better and reducing the risk of overfitting. The framework supports multiple classification tasks, enabling the identification and interpretation of complex patterns in images for efficient and reliable classification. To validate its dependability and efficiency in predictive analytics, the system is rigorously evaluated using critical performance indicators such as accuracy, precision, recall, and F1-score. By leveraging large-scale datasets and refining computational models, this solution provides a scalable and automated diagnostic support tool, enhancing decision-making processes across various applications.

Keywords

References (10)

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