Cross-Domain Deep Learning Techniques for Enhanced Diagnostic and Phenotyping in Medical and Agricultural Imaging: Bridging Computer Science and Healthcare Management

Abstract

In this study, deep learning has been investigated in clinical analysis, in terms of increasing the precision and speed in diseases identification, image division and features identification in various systems of bioscience. The CNN, U-net, and GAN deep learning algorithms used have been developed to accomplish a range of image and classification processes. The CNN-based model in healthcare was 92% diagnostically accurate, and the U-Net model was 89% accurate in segmenting medical images. In agriculture, CNN model when applied to plant disease detection had an accuracy of 87%; GANs were utilized for generation of synthetic data thus enhancing the performance of model training. Stakeholders’ findings demonstrate that the application of AI can considerably decrease diagnostic time and improve accuracy in most cases in both fields. Synthetic data generation also played a big role in avoiding issues caused by the small, labeled datasets, especially in terms of generalization of models. It highlights revolutionizing the diagnostic process using cross-domain deep learning applications and provides information for a new era of healthcare management and agriculture.