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Towards Designing an Automated Classification of Lymphoma subtypes using Deep Neural Networks

Published: 03 January 2019 Publication History

Abstract

Cancer diagnosis and treatment is a field where AI has the potential to provide tremendous scope for targeted large scale interventions. NITI Aayog, Government of India, in its discussion paper, mentions that there are over 1 million new cases of cancer in India every year, a number which is likely to rise given the annual rise in population. However, the number of pathologists experienced in oncology are few, nearly as much as a thousandth of the patients suffering from cancer, which leads to delay in cancer diagnosis and treatment. Machine learning, when used with image digitization techniques that have progressed during the last decade, can help bridge this divide. Traditionally, the initial screening and diagnosis of cancer is done using morphological analysis, which can be automated using machine learning techniques. In our work, we attempt to illustrate the potential of Deep Learning, a sub domain of Machine Learning, in contributing to the automation of cancer diagnosis, by proposing a method to classify the subtypes of lymphoma, a cancer of the lymph nodes. We implement our method on an NIA curated dataset consisting of three labeled subtypes of lymphoma namely chronic lymphocytic leukemia, follicular lymphoma and mantle cell lymphoma. The method proposed in this work, which yields an accuracy of 97.33% on the NIA curated dataset shows that deep learning has the potential to aid an automated diagnosis for lymphoma.

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N. V. Orlov et al., "Automatic Classification of Lymphoma Images With Transform-Based Global Features," in IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 4, pp. 1003--1013, July 2010.
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  • (2022)A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future ProspectsJournal of Medical Internet Research10.2196/3649024:7(e36490)Online publication date: 12-Jul-2022
  • (2022)Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)10.1109/SETIT54465.2022.9875482(447-451)Online publication date: 28-May-2022
  • (2022)Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels methodNeural Computing and Applications10.1007/s00521-022-07183-834:16(13499-13512)Online publication date: 1-Aug-2022
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      cover image ACM Other conferences
      CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data
      January 2019
      380 pages
      ISBN:9781450362078
      DOI:10.1145/3297001
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 03 January 2019

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      Author Tags

      1. Artificial neural network
      2. Multi-layer neural network
      3. Supervised learning

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      CoDS-COMAD '19
      CoDS-COMAD '19: 6th ACM IKDD CoDS and 24th COMAD
      January 3 - 5, 2019
      Kolkata, India

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      CODS-COMAD '19 Paper Acceptance Rate 62 of 198 submissions, 31%;
      Overall Acceptance Rate 197 of 680 submissions, 29%

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      Cited By

      View all
      • (2022)A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future ProspectsJournal of Medical Internet Research10.2196/3649024:7(e36490)Online publication date: 12-Jul-2022
      • (2022)Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)10.1109/SETIT54465.2022.9875482(447-451)Online publication date: 28-May-2022
      • (2022)Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels methodNeural Computing and Applications10.1007/s00521-022-07183-834:16(13499-13512)Online publication date: 1-Aug-2022
      • (2020)Accurate Recognition of Leukemia Sub-types by Utilizing a Transfer Learned Deep Convolutional Neural Network2020 11th International Conference on Electrical and Computer Engineering (ICECE)10.1109/ICECE51571.2020.9393031(427-430)Online publication date: 17-Dec-2020
      • (2020)Histopathological Image and Lymphoma Image Classification using customized Deep Learning models and different optimization algorithms2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225616(1-7)Online publication date: Jul-2020

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