Special Issue "Cancer Diagnosis Using Machine Learning and Artificial Intelligence"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2023

Special Issue Editors

1 Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia 2 Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia
Interests: biomedical imaging; image processing; digital signal processing; artificial intelligence; feature extraction; recognition and classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Cancer diagnosis is a crucial aspect of modern medicine, with early detection being crucial for successful treatment. The traditional diagnostic methods, such as biopsy and imaging, rely on the expertise of clinicians to identify and interpret cancerous cells. However, these methods are often time-consuming, invasive, and subjective, leading to inaccurate diagnoses and delays in treatment.

Machine learning (ML) and artificial intelligence (AI) offer promising solutions to these issues by providing automated, accurate, and objective cancer diagnoses. ML algorithms can analyze large amounts of patient data and identify complex patterns that may not be discernible to human clinicians. AI can also incorporate patient data and other sources of medical information to create personalized cancer treatment plans, leading to improved outcomes for patients.

The use of ML and AI in cancer diagnosis is a rapidly evolving field with significant potential for impacting patient care. Researchers are exploring the use of various ML algorithms, such as support vector machines, decision trees, and neural networks, to develop accurate cancer-diagnosis models. Additionally, AI systems can be trained on large-scale databases of patient information to identify novel biomarkers that can aid in early cancer detection and personalized treatment planning.

The development of ML- and AI-based cancer-diagnosis systems requires collaboration between clinicians, data scientists, and machine-learning experts. The systems must be validated on large datasets of patient information and evaluated for their accuracy, sensitivity, and specificity. The ultimate goal is to develop reliable, cost-effective, and scalable cancer-diagnosis tools that can improve patient outcomes and reduce the burden on healthcare systems.

In conclusion, the use of machine learning and artificial intelligence in cancer diagnosis is a promising area of research that has the potential to transform cancer care. The development of accurate and objective cancer-diagnosis models will aid clinicians in making timely and informed treatment decisions, leading to improved outcomes for patients.

Dr. Wan Azani Mustafa
Dr. Hiam Alquran
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Cancer Image/Biosignal Analysis
  • Cancer Image Segmentation/Detection
  • Healthcare Cancer Systems
  • AI-Based Cancer Image Registration
  • Cancer Image Recognition
  • Diagnostic Aid
  • AI-Based Screening System
  • Oncology Image and Signal Classification
  • Biomedical Image Retrieval
  • Medical Image Annotation
  • Biomedical Image Summarization/Filtering
  • Cancer Diagnosis
  • Machine Learning and Deep Learning
  • Artificial Intelligence in Cancer
  • AI-Based Medical Image Diagnosis
  • Medical Deep-Learning CAD Systems
  • XAI-Based Medical Imaging
  • Patient/Treatment Stratification Based On AI Image Processing
  • Synthetic Medical Image Generation
  • Explainable AI in Medicine

Published Papers

This special issue is now open for submission.
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