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

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

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• 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