IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0269826.html
   My bibliography  Save this article

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity

Author

Listed:
  • Sidratul Montaha
  • Sami Azam
  • A K M Rakibul Haque Rafid
  • Sayma Islam
  • Pronab Ghosh
  • Mirjam Jonkman

Abstract

The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data.

Suggested Citation

  • Sidratul Montaha & Sami Azam & A K M Rakibul Haque Rafid & Sayma Islam & Pronab Ghosh & Mirjam Jonkman, 2022. "A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-49, August.
  • Handle: RePEc:plo:pone00:0269826
    DOI: 10.1371/journal.pone.0269826
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269826
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0269826&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0269826?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lingzhi Kong & Jinyong Cheng, 2021. "Based on improved deep convolutional neural network model pneumonia image classification," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-18, November.
    2. Pierre Fontanillas & Babak Alipanahi & Nicholas A. Furlotte & Michaela Johnson & Catherine H. Wilson & Steven J. Pitts & Robert Gentleman & Adam Auton, 2021. "Disease risk scores for skin cancers," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Shaode Yu & Shibin Wu & Lei Wang & Fan Jiang & Yaoqin Xie & Leida Li, 2017. "A shallow convolutional neural network for blind image sharpness assessment," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-16, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    2. Bhanuprakash Dudi & V. Rajesh, 2022. "Optimized threshold-based convolutional neural network for plant leaf classification: a challenge towards untrained data," Journal of Combinatorial Optimization, Springer, vol. 43(2), pages 312-349, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0269826. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.