A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics

• Published in: Journal of multidisciplinary healthcare Vol. 17; pp. 4411 - 4425
• Main Authors: Sriraman, Harini, Badarudeen, Saleena, Vats, Saransh, Balasubramanian, Prakash
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2024; Taylor & Francis Ltd; Dove Medical Press
• Subjects: Accuracy; Algorithms; Artificial intelligence; Cancer; Cancer research; Cancer therapies; classification; Climate change; cnn; Data mining; Deep learning; Diagnosis; Disease; Educational evaluation; elastography; Endoscopy; feedforward neural network; healthcare; Illnesses; Image processing; Immunotherapy; Infrastructure; Literature reviews; Machine learning; Mammography; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical research; Neural networks; real-time diagnosis; Systematic review; India; image processing; CNN; feedforward neural network; AI; DL; elastography; real-time diagnosis; classification; machine learning; artificial intelligence; healthcare
• ISSN: 1178-2390; 1178-2390
• DOI: 10.2147/JMDH.S446745

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.