IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v3y2021i4p110-118.html
   My bibliography  Save this article

Towards Skin Cancer Classification Using Machine Learning And Deep Learning Algorithms: A Comparison

Author

Listed:
  • Iqra Khan

    (Department of Computer Science (National Textile University).)

  • Muhammad Zohaib Siddique

    (Department of Computer Science (Riphah International University).)

  • Ateeq Ur Rehman Butt

    (Department of Computer Science (National Textile University).)

  • AZHAR IMRAN Mudassir

    (Department of Creative Technologies (Air University Islamabad).)

  • Muhammad Azeem Qadir

    (Department of Computer Science (National Textile University).)

  • Sundus Munir

    (Department of Computer Science (Lahore Garrison University).)

Abstract

Skin cancer is an uncontrolled development of abnormal skin cells potentially due to excessive exposure to sun, history of sunburns, less melanin, Precancerous skin lesions, moles, etc. This occur when unrepaired DNA damages the cells of the skin. It is one of the diseases that are viewed on its quick evolution and the most common type of cancer that endangers life. Researchers have implemented several machine learning and deep learning techniques for classification of skin cancer. In this research paper, different cancer categories are classified using significant attributes. We have used International Skin Imaging Collaboration (ISIC) dataset for classification purposes. This dermoscopic attributes dataset includes 1000 images and 10016 instances, seven categories, 5 features and 2 Meta attributes. We implemented K-Nearest Neighbor, Logistic Regression, Convolutional Neural Network, Naïve Bayes, and Decision Tree for classification and compared their performance. In order to implement classification algorithm, we used Orange which is an open-source machine learning, data mining, and data visualization toolkit. The models are evaluated based on matrices that include Accuracy, C. Automation, F1 score, Precision, Recall, and AUC. Furthermore, frequency of features is visualized using graphical method and the ROC analysis is also performed for the classifiers. It is observed that CNN technique provided the highest accuracy of 89% and the mentioned results are the highest results of classification with the state of the art techniques. For future, the improved and recent dataset and ensemble modelling techniques based on deep learning can used to enhance classification results. The research can also be extended for other cancer types using CNN.

Suggested Citation

  • Iqra Khan & Muhammad Zohaib Siddique & Ateeq Ur Rehman Butt & AZHAR IMRAN Mudassir & Muhammad Azeem Qadir & Sundus Munir, 2021. "Towards Skin Cancer Classification Using Machine Learning And Deep Learning Algorithms: A Comparison," International Journal of Innovations in Science & Technology, 50sea, vol. 3(4), pages 110-118, December.
  • Handle: RePEc:abq:ijist1:v:3:y:2021:i:4:p:110-118
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Adeel Abbasa & Zeshan Iqbal, 2022. "Double Auction used Artificial Neural Network in Cloud Computing," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 65-76, June.
    2. Rashid Amin & Muzammal Majeed & Farrukh Shoukat Ali & Adeel Ahmed & Mudassar Hussain, 2022. "Reliability Awareness Multiple Path Installation in Software Defined Networking using Machine Learning Algorithm," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 158-172, July.
    3. Sabina Irum & Jamal Abdul Nasir & Zakia Jalil, 2022. "What have you read? based Multi-Document Summarization," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 94-102, June.

    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:abq:ijist1:v:3:y:2021:i:4:p:110-118. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Hafiz Haroon Ahmad, Iqra Nazeer (email available below). General contact details of provider: .

    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.