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  • 1.
    Wieslander, Håkan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Forslid, Gustav
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Hirsch, Jan-Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery.
    Runow Stark, Christina
    Swedish Dent Serv, Med Dent Care, Sodersjukhuset, Sweden.
    Kecheril Sadanandan, Sajith
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Deep convolutional neural networks for detecting cellular changes due to malignancy2017In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017), IEEE, 2017, p. 82-89Conference paper (Refereed)
    Abstract [en]

    Discovering cancer at an early stage is an effective way to increase the chance of survival. However, since most screening processes are done manually it is time inefficient and thus a costly process. One way of automizing the screening process could be to classify cells using Convolutional Neural Networks. Convolutional Neural Networks have been proven to be accurate for image classification tasks. Two datasets containing oral cells and two datasets containing cervical cells were used. For the cervical cancer dataset the cells were classified by medical experts as normal or abnormal. For the oral cell dataset we only used the diagnosis of the patient. All cells obtained from a patient with malignancy were thus considered malignant even though most of them looked normal. The performance was evaluated for two different network architectures, ResNet and VGG. For the oral datasets the accuracy varied between 78-82% correctly classified cells depending on the dataset and network. For the cervical datasets the accuracy varied between 84-86% correctly classified cells depending on the dataset and network. The results indicate a high potential for detecting abnormalities in oral cavity and in uterine cervix. ResNet was shown to be the preferable network, with a higher accuracy and a smaller standard deviation.

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