Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images

Abstract

Cancer develops when a single or a group of cells grows and spreads uncontrollably. Histopathology images are used in cancer diagnosis since they show tissue and cell structures under a microscope. Knowledge-based and deep learning-based computer-aided detection is an ongoing research field in cancer diagnosis using histopathology images. Feature extraction is vital in both approaches since the feature set is fed to a classifier and determines the performance. This paper evaluates three feature extraction methods and their performance in breast cancer diagnosis. Features are extracted by (1) a Convolutional Neural Network, (2) a transfer learning architecture VGG16, and (3) a knowledge-based system. The feature sets are tested by seven classifiers, including Neural Network (64 units), Random Forest, Multilayer Perceptron, Decision Tree, Support Vector Machines, K-Nearest Neighbors, and Narrow Neural Network (10 units) on the BreakHis 400× image dataset. The CNN achieved up to 85% for the Neural Network and Random Forest, the VGG16 method achieved up to 86% for the Neural Network, and the knowledge-based features achieved up to 98% for Neural Network, Random Forest, Multilayer Perceptron classifiers.

Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction

“…Their technique had a predictive accuracy of 90%, showing its effectiveness in predicting BC prognosis from HPI and clinical factors. Kode and Barkana (2023) showed the efficacy of their technique for extracting features and evaluating performance in breast HPI [8]. They documented feature extraction accuracies between 80% and 95% on several histopathology image datasets, demonstrating the strength of their method.…”

Exploring the Impact of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Esthesioneuroblastomas: A Comprehensive Review

“…The network adjusts its filters to recognize patterns such as irregular nuclei or changes in the cytoplasm that are indicative of malignant cells [34]. The network comprises convolutional layers, which apply filters to detect important visual patterns such as edges or specific textures.…”

“…The network comprises convolutional layers, which apply filters to detect important visual patterns such as edges or specific textures. These are followed by pooling layers that reduce the spatial size of the feature maps [26,34]. This reduction decreases the number of parameters and computations needed, thus simplifying the network while retaining the critical features necessary for classification.…”