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Abstract:
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Microscopic analysis of biopsy slides, particularly
immunohistochemistry (IHC), plays a critical role in cancer
diagnosis. This process still largely relies on the visual assessment
and cell counting, which are time-consuming and subject to error
and variability. Recent advances in Deep Learning, especially
convolutional neural networks (CNNs), have enabled the development
of interesting architectures for cell detection, counting,
and classification, providing robustness and standardization for
histopathological analysis. In this paper we present a comparative
study of CNN architectures applied to the analysis of microscopic
biopsy images in breast cancer pathology. The proposed approach
considers object detection (OB) and object classification (OC)
paradigms. For OB, a Faster R-CNN framework with a ResNet-50
backbone and a Feature Pyramid Network (FPN) was considered.
For OC, architectures such as DenseNet-121 were evaluated
due to their dense connectivity, which promotes efficient feature
reuse and improved representation of fine-grained textural
patterns. The experimental results were applied in a prepared
environment, where dataset partitioning, data augmentation,
normalization, and evaluation were considered, as well as the
objective evaluation metrics mean Average Precision (mAP),
precision, recall, F1-score, and cell counting error. The obtained
experimental results indicate the RESNET as the most suitable
approach for multi-class classification with 82.89% precision,
suggesting it as a promising solution for diagnosis in microscopic
biopsy analysis. |
