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Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database

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Abstract

Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in the histopathology dataset are an essential parameter in disease detection. The nucleus segmentation was performed using the colorectal histology MNIST dataset for nucleus detection in this study. The graph theory, PSO, watershed, and random walker algorithms were used for the segmentation process. In addition, we present the 10-class MedCLNet visual dataset consisting of the NCT-CRC-HE-100 K dataset, LC25000 dataset, and GlaS dataset that can be used in transfer learning studies from deep learning techniques. The study proposes a transfer learning technique using the MedCLNet database. Deep neural networks pre-trained with the proposed transfer learning method were used in the classification with the colorectal histology MNIST dataset in the experimental process. DenseNet201, DenseNet169, InceptionResNetV2, InceptionV3, ResNet152V2, ResNet101V2, and Xception deep learning algorithms were used in transfer learning and the classification studies. The proposed approach was analyzed before and after transfer learning with different methods (DenseNet169 + SVM, DenseNet169 + GRU). In the performance measurement, using the colorectal histology MNIST dataset, 94.29% accuracy was obtained in the DenseNet169 model, which was initiated with random weights in the multi-classification study, and 95.00% accuracy after transfer learning was applied. In comparison with the results obtained from empirical studies, it was demonstrated that the proposed method produced satisfactory outcomes. The application is expected to provide a secondary evaluation for physicians in colon cancer detection and the segmentation.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Gumushane University, Gumushane, 2900, Turkey

    Hatice Catal Reis

  2. Department of Computer Engineering, University of Harran, Sanliurfa, Turkey

    Veysel Turk

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  1. Hatice Catal Reis
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Hatice Catal Reis and Veysel Turk. The first draft of the manuscript was written by Hatice Catal Reis, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hatice Catal Reis.

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Reis, H.C., Turk, V. Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database. J Digit Imaging 36, 306–325 (2023). https://doi.org/10.1007/s10278-022-00701-z

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