Browsing by Author "Castro Ortega, Kurt"
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Item Histology Tissue Classification for Colorectal Cancer: Deep Learning Approaches for Medical Image Analysis. Which deep learning architecture provides the best balance between accuracy and computational efficiency for histological tissue classification in colorectal cancer?(Universidad del Desarrollo. Facultad de Ingeniería, 2025) Castro Ortega, Kurt; Gómez Vargas, Germán; Michelucci, UmbertoThis report presents a comprehensive comparative analysis of deep learning architectures for the classification of histological images of colorectal cancer, a fundamental task in the emerging field of digital pathology. Automating this process through the use of deep learning models not only promises to accelerate diagnosis, but also to improve its accuracy and consistency, representing a strategic opportunity to optimize clinical workflows and advance toward precision medicine. The project addressed the classification of eight types of colorectal cancer tissue through a systematic comparative evaluation of five deep learning architectures: a baseline Convolutional Neural Network (CNN) trained from scratch and four advanced models (VGG19, ResNet50, EfficientNetB0, and Vision Transformer ViT-B16) implemented with the Transfer Learning strategy. The results showed that the ResNet50 architecture achieved the highest performance, with an accuracy of 95.07% on the test set. The analysis validated the project's central hypothesis: pre-trained architectures significantly outperformed the model trained from scratch, underscoring the effectiveness of transfer learning in domains with limited data. Furthermore, the study revealed a complex balance between performance and computational efficiency, where theoretically more efficient models such as EfficientNetB0 do not always translate into the lowest inference latency on specific hardware. This report provides a detailed analysis of these findings, offering empirical guidance for architecture selection in computational pathology applications.