Histopathology currently serves as the standard for breast
cancer diagnosis, but its manual execution demands time and expertise
from pathologists. Artificial intelligence, particularly in digital pathology, has made significant strides, offering new opportunities for precision
and efficiency in disease diagnosis. This study presents a methodology to
enhance cell nuclei detection in breast cancer histopathological images
using convolutional neural network models to apply super-resolution and
object detection. Several model architectures are explored, and their performance is evaluated regarding accuracy and sensitivity. The results
affirm the potential of the proposed approach for automated cell nuclei
identification. These AI advancements in digital pathology open avenues
for early and precise cancer detection, influencing clinical practices and
patient well-being and improving diagnostic efficiency.
