Deep Learning Applications in Medical Image Processing: A Comparative Study of CNN Architectures

Abstract

This study investigates the applications of deep learning, specifically Convolutional Neural Networks (CNNs), in the field of medical image processing. The primary objective is to compare various CNN architectures to evaluate their effectiveness in tasks such as image classification, segmentation, and detection. Different CNN models, including traditional architectures and advanced variants, were tested on medical datasets, including radiological and histopathological images. The research method involved training and evaluating these models using standard performance metrics such as accuracy, sensitivity, specificity, and computational efficiency. The findings reveal that advanced CNN architectures outperform traditional models in terms of accuracy and computational speed, especially when handling complex medical image features. Furthermore, the study highlights the potential of deep learning techniques to enhance diagnostic accuracy and aid in early disease detection. The implications of these findings suggest that CNNs have a significant impact on improving medical image analysis, offering a promising solution for healthcare professionals in diagnosing and monitoring various medical conditions.