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Disease Diagnosis with Medical Imaging Using Deep Learning

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Advances in Information and Communication (FICC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 439))

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Abstract

Faster, augmented, remote and digital diagnostic prediction models represent the future of preventive and affordable medical healthcare for patients world-wide. A premature detection can help doctors to prevent, reduce or stop the disease evolution over time. This paper proposes Deep Learning methods for diagnosis detection starting from an existing ResUNet architecture and over that creating a new one through replacing ResUNet blocks with a kind of Inception blocks. The proposal is applied over a semantic segmentation task offering a comparative study seeing the differences given the proposed architecture. For this study, also we used Transfer Learning technique improve to speed up and increase the accuracy of detecting and localizing brain tumors based on magnetic resonance imaging (MRI) scans to help in early diagnosis of tumors which would primordially be a life saver. This paper explores a new detection architecture for imagistic diagnostic forecast and prediction.

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Author information

Authors and Affiliations

  1. IBM Romania, Bucuresti, Romania

    Marina-Adriana Mercioni

  2. IBM USA, New York, USA

    Lucia Larise Stavarache

Authors
  1. Marina-Adriana Mercioni
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  2. Lucia Larise Stavarache
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Correspondence to Marina-Adriana Mercioni .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Mercioni, MA., Stavarache, L.L. (2022). Disease Diagnosis with Medical Imaging Using Deep Learning. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-030-98015-3_13

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