A novel transfer learning approach for the classification of histological images of colorectal cancer
| Autor: | Elene Firmeza Ohata, Gabriel M. Bezerra, Victor Hugo C. de Albuquerque, Mohammad Mehedi Hassan, Pedro Pedrosa Rebouças Filho, João Victor Souza das Chagas |
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| Rok vydání: | 2021 |
| Předmět: |
Colorectal cancer
business.industry Computer science Cancer Pattern recognition Histology medicine.disease Convolutional neural network Theoretical Computer Science Random forest Support vector machine Naive Bayes classifier Hardware and Architecture Multilayer perceptron medicine Artificial intelligence business Transfer of learning Software Information Systems |
| Zdroj: | The Journal of Supercomputing. 77:9494-9519 |
| ISSN: | 1573-0484 0920-8542 |
| Popis: | Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation. |
| Databáze: | OpenAIRE |
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