Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images
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 cance...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/12/3075 |
| _version_ | 1797595582639898624 |
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| author | Hepseeba Kode Buket D. Barkana |
| author_facet | Hepseeba Kode Buket D. Barkana |
| author_sort | Hepseeba Kode |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:39:33Z |
| format | Article |
| id | doaj.art-34e04971d5f44739bf4f513adc42dde6 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T02:39:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-34e04971d5f44739bf4f513adc42dde62023-11-18T09:40:20ZengMDPI AGCancers2072-66942023-06-011512307510.3390/cancers15123075Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology ImagesHepseeba Kode0Buket D. Barkana1Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USAElectrical Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USACancer 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.https://www.mdpi.com/2072-6694/15/12/3075CNNsVGG16breast cancerhistopathologyknowledge-basedfeature extraction |
| spellingShingle | Hepseeba Kode Buket D. Barkana Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images Cancers CNNs VGG16 breast cancer histopathology knowledge-based feature extraction |
| title | Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images |
| title_full | Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images |
| title_fullStr | Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images |
| title_full_unstemmed | Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images |
| title_short | Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images |
| title_sort | deep learning and expert knowledge based feature extraction and performance evaluation in breast histopathology images |
| topic | CNNs VGG16 breast cancer histopathology knowledge-based feature extraction |
| url | https://www.mdpi.com/2072-6694/15/12/3075 |
| work_keys_str_mv | AT hepseebakode deeplearningandexpertknowledgebasedfeatureextractionandperformanceevaluationinbreasthistopathologyimages AT buketdbarkana deeplearningandexpertknowledgebasedfeatureextractionandperformanceevaluationinbreasthistopathologyimages |