Impact of Training Data Size on Model Accuracy and Computational Efficiency in Deep Learning Based Medical Image Diagnosis

Abstract

The rapid advancement of deep learning has meaningfully improved medical image diagnosis and as well as offering enhanced accuracy and efficiency in detecting complex diseases. Though training data size effects on model performance and computational efficiency remains a critical area of investigation. This research study discovers how varying the size of training datasets influences the diagnostic accuracy and computational cost of deep learning models used in medical imaging Employing convolutional neural networks (CNNs) on test datasets, varying dataset sizes were experimented upon in order to gauge changes in training time, accuracy, and utilization of resources. The results confirm a linear relation between model accuracy and dataset size up to some point beyond which marginal returns lose their utility. Additionally, larger datasets increase computational demands, affecting training time and memory usage. The study emphasizes the importance of identifying an optimal dataset size that balances accuracy and computational efficiency, especially in resource-constrained clinical environments. These type of vision & insights are crucial for developing scalable and effective AI-based diagnostic tools in healthcare. Results of this research study support future research and practical deployment of deep learning in medical image analysis, highlighting the trade-offs between data volume accuracy and as well as efficiency.

Key words: Data Size – Data Model – Learning – Diagnosis – Computational Efficiency