The Effectiveness of Artificial Neural Networks in Early Diagnosis Based on Medical Imaging

Abstract

The integration of artificial neural networks (ANNs), particularly convolutional neural networks (CNNs), into medical imaging has fundamentally transformed the paradigm of early disease diagnosis. This comprehensive review systematically examines the architecture, functionality, and clinical effectiveness of ANN-based models applied to a diverse spectrum of medical imaging modalities, encompassing mammography, magnetic resonance imaging (MRI), computed tomography (CT), fundus photography, and dermoscopy. Evidence synthesized from the current literature demonstrates that state-of-the-art deep learning architectures—including ResNet, VGG, DenseNet, InceptionV3, and U-Net—consistently achieve diagnostic accuracy exceeding 93% across oncological, neurological, ophthalmological, and pulmonary applications. Noteworthy findings include a 98.2% classification accuracy for brain tumor MRI analysis, 97.4% for breast cancer mammographic detection, and area under the receiver operating characteristic curve (AUC) values surpassing 0.97 in multi-class diagnostic tasks. The paper further critically evaluates the comparative performance of leading architectures through structured tables and graphical analyses, highlights the role of transfer learning in addressing data scarcity, and identifies persistent challenges including model interpretability, data heterogeneity, class imbalance, and regulatory compliance barriers. Future directions toward federated learning, explainable AI (XAI), and multimodal fusion frameworks are discussed as pathways to clinically deployable ANN-based diagnostic tools.