Evaluating Modern CNN Architectures for Advancements in Healthcare Applications

Abstract

Convolutional Neural Networks (CNNs) have become a driving force in healthcare AI, enabling machines to analyze complex medical data with remarkable accuracy. From detecting tumors in MRI scans to classifying skin conditions and diagnosing retinal diseases, CNNs have transformed how medical professionals interpret visual information. This paper presents a comparative analysis of modern CNN architectures, examining their design principles, computational efficiency, and performance in diverse healthcare applications. Models such as LeNet, AlexNet, VGGNet, ResNet, DenseNet, MobileNet, and EfficientNet are evaluated in terms of their suitability for tasks like radiology image classification, histopathology segmentation, and real-time mobile diagnostics. By focusing on their application in the medical domain, this study highlights the strengths, limitations, and practical trade-offs of each architecture. The paper also discusses key challenges such as data scarcity, interpretability, and ethical concerns, while exploring future trends like federated learning, edge deployment, and hybrid vision-language models in clinical settings. The goal is to help researchers and healthcare practitioners choose the most appropriate CNN architecture for safe, efficient, and scalable medical AI solutions.