Authors: Shalini Nair
Abstract: Histopathological image analysis is a critical process in diagnosing a wide range of diseases, particularly cancers. Traditionally, it relies heavily on the expertise of pathologists to interpret tissue samples under a microscope. However, this manual approach is time-consuming, subject to inter-observer variability, and limited by human fatigue. Deep learning (DL), a subset of artificial intelligence, offers transformative potential in histopathology by automating image interpretation with high accuracy and consistency. This paper explores the applications of deep learning in histopathological image analysis, focusing on convolutional neural networks (CNNs), segmentation techniques, classification models, and recent advances in digital pathology. Challenges, such as data heterogeneity, annotation bottlenecks, and model interpretability, are discussed alongside future prospects for integrating DL into routine clinical workflows to improve diagnostic precision and patient outcomes.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.557
