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Transforming view of medical images using deep learning

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Abstract

Since the last decade, there is a significant change in the procedure of medical diagnosis and treatment. Specifically, when internal tissues, organs such as heart, lungs, brain, kidneys and bones are the target regions, a doctor recommends ‘computerized tomography’ scan and/or magnetic resonance imaging to get a clear picture of the damaged portion of an organ or a bone. This is important for correct examination of the medical deformities such as bone fracture, arthritis, and brain tumor. It ensures prescription of the best possible treatment. But ‘computerized tomography’ scan exposes a patient to high ionizing radiation. These rays make a person more prone to cancer. Magnetic resonance imaging requires a strong magnetic field. Thus, it becomes impractical for patients with implants in their body. Moreover, the high cost makes the above-stated techniques unaffordable for low economy class of society. The above-mentioned challenges of ‘computerized tomography’ scan and magnetic resonance imaging motivate researchers to focus on developing a technique for conversion of 2-dimensional view of medical images into their corresponding multiple views. In this manuscript, the authors design and develop a deep learning model that makes an effective use of conditional generative adversarial network, an extension of generative adversarial network for the transformation of 2-dimensional views of human bone into the corresponding multiple views at different angles. The model will prove useful for both doctors and patients.

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Acknowledgements

We would like to thank Dr. Nand Kishore Poonia, Managing Director, Sir Chhotu Ram Dana Shivam Hospital, Jaipur, for providing the dataset.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Nitesh Pradhan

  2. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Vijaypal Singh Dhaka & Geeta Rani

  3. Department of Electronics and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Himanshu Chaudhary

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  1. Nitesh Pradhan
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  2. Vijaypal Singh Dhaka
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  3. Geeta Rani
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  4. Himanshu Chaudhary
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Correspondence to Geeta Rani.

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Pradhan, N., Dhaka, V.S., Rani, G. et al. Transforming view of medical images using deep learning. Neural Comput & Applic 32, 15043–15054 (2020). https://doi.org/10.1007/s00521-020-04857-z

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  • DOI: https://doi.org/10.1007/s00521-020-04857-z

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