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Combined Radiology and Pathology Based Classification of Tumor Types

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Deep Learning for Cancer Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 908))

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Abstract

Computer Aided Detection plays a crucial role in the early detection of deadly diseases such as cancer (or) tumor. Pathology and radiology images form the core of tumor diagnosis. Pathology images provide clinical information about the tissues whereas the radiology images can be used for locating the lesions. This work aims at proposing a classification model which categorizes the tumor as oligodendroglioma (benign tumors) (or) astrocytoma (Malignant tumors) using features of both the radiology and pathology images. Dataset from MICCAI Computational Precision Medicine Challenge is used for model building. The proposed model uses dedicated workflows for processing the pathology and radiology images. The feature descriptors of the images are obtained using pre-trained Inception v3 model. The resulting vectors are then used as input to the linear SVM (Support Vector Machine) classification model. The SVM model provided an accuracy of 75% on the blind folded test dataset provided in the competition.

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Authors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    N. Ravitha Rajalakshmi, B. Sangeetha & R. Vidhyapriya

  2. PSG College of Technology, Coimbatore, Tamil Nadu, India

    Nikhil Ramesh

Authors
  1. N. Ravitha Rajalakshmi
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  2. B. Sangeetha
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  3. R. Vidhyapriya
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  4. Nikhil Ramesh
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Corresponding author

Correspondence to N. Ravitha Rajalakshmi .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

    Jafar Alzubi

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Ravitha Rajalakshmi, N., Sangeetha, B., Vidhyapriya, R., Ramesh, N. (2021). Combined Radiology and Pathology Based Classification of Tumor Types. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_6

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