Design and Implementation of a Deep Learning Imaging System for Early Disease Detection

Abstract

Deep learning has revolutionized medical imaging and early disease diagnosis by enabling machines to learn complex hierarchical representations from high-dimensional clinical data. This paper proposes a deep neural network–based framework that integrates multimodal medical imaging data, such as MRI, CT, and ultrasound scans, to enhance the accuracy and efficiency of early disease detection. The model employs a hybrid convolutional and transformer-based architecture that extracts spatial and contextual features simultaneously. To address the challenges of limited labeled medical data, a transfer learning strategy is applied using pre-trained models on large-scale datasets, followed by fine-tuning on specific medical domains. Experimental evaluations on benchmark datasets demonstrate that the proposed model significantly improves diagnostic accuracy, sensitivity, and specificity compared with conventional convolutional networks and handcrafted-feature-based methods. The results indicate that the integration of deep learning with medical imaging offers a powerful and scalable solution for intelligent healthcare systems.