Enhancing AI-Driven Pathology Image Analysis Using Convolutional Neural Networks and Transfer Learning Techniques

Abstract

This research paper investigates the potential of enhancing AI-driven pathology image analysis through the integration of convolutional neural networks (CNNs) and transfer learning techniques. Pathology image analysis is critical for accurate disease diagnosis, yet it remains a complex task due to the high variability in histopathological slides. We propose a hybrid framework that leverages CNN architectures known for their proficiency in image recognition and transfer learning strategies to improve model performance with limited labeled data. The study systematically evaluates different CNN architectures, including VGG, ResNet, and Inception, to identify the most effective model for extracting salient features from pathology images. Additionally, we explore various transfer learning methodologies, such as fine-tuning and feature extraction, to optimize model training efficiency and accuracy. Our experiments are conducted on benchmark datasets, including the CAMELYON16 and TCGA collections, providing comprehensive empirical evidence of our approach's effectiveness. Results indicate that our proposed framework significantly outperforms traditional methods, achieving a notable increase in classification accuracy and reduced computation time. The findings highlight the combined power of CNNs and transfer learning in advancing pathology image analysis, offering promising implications for clinical diagnostics and personalized medicine. This paper concludes with a discussion on the challenges and future research directions in deploying AI technologies within the clinical pathology domain.