Deep Learning in Cancer Prognosis Prediction: Challenges and Applications
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 2619
Special Issue Editors
Interests: deep learning; artificial intelligence
Interests: applied mathematics; fluid mechanics; convective instability problems
Special Issue Information
Dear Colleagues,
Deep Learning, a subset of Machine Learning that possesses the ability to enhance the recognition, extraction and categorization of features through Artificial Neural Networks (ANN), thus, generating highly accurate predictions, has a wide range of applications in different medical domains such as health care, medical imaging and clinical development. Cancer (disease) is the second leading cause of death in the world. Hence, its early identification and its prognosis can be helpful with respect to saving patients' lives and understanding the necessary clinical assistance to be provided. Cancer prognosis refers to the estimation of disease severity and its most likely outcome, which in turn, provides an idea of the patient survival rate. It plays a vital role in decision making regarding treatments and patient management. The advanced applications of statistical analysis and refinement in biomedical research have been the impelling cause for improving cancer prognosis prediction. The latest noteworthy advancements in Artificial Intelligence, particularly in Deep Learning, and escalation in computational capacity enable the prospect of developing more precise models for explicit cancer prognosis prediction. Moreover, the wide range of accessibility to open-source databases for acquiring such data has been a catalyst for the process. While dealing with excessively large amounts of data, Deep Learning has proved to be an exceptionally better choice, providing higher accuracies than existing traditional methods. Cancer prognosis prediction with the implementation of Deep Learning has greatly outperformed current approaches such as Discriminant Analysis and Cox Proportional Hazards. As a result, Deep Learning is believed to potentially impact cancer prognosis prediction in a greater and more positive manner, considering the burst of transcriptomics and genomics data. This Special Issue will accept a wide range of studies, technology developments, theoretical methodologies, experimental frameworks, etc., related to cancer prognosis prediction.
Dr. Damodar Reddy Edla
Dr. Ravi Ragoju
Dr. Ramalingaswamy Cheruku
Guest Editors
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Keywords
- deep learning
- cancer prognosis prediction
- classification feature
- extraction auto-encoders
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