ADVANCES IN MELANOMA DETECTION: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES AND TECHNIQUES

Vinay S. Nalawade; Shailesh Kumar; Shrinivas T. Shirkande

doi:10.29121/shodhkosh.v5.i6.2024.4824

Authors

Vinay S. Nalawade Research Scholar, Department of Computer Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, India.
Dr. Shailesh Kumar Associate Professor, Department of Computer Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, India.
Dr.Shrinivas T. Shirkande Principal & Associate Professor, S B Patil College of Engineering, Indapur, Pune, Maharashtra, India.

DOI:

https://doi.org/10.29121/shodhkosh.v5.i6.2024.4824

Keywords:

Melanoma Detection, Artificial Intelligence, Imaging Technologies, Deep Learning, Biomarkers

Abstract [English]

Melanoma detection has come a long way, mostly thanks to breakthroughs in image technologies and machine learning techniques that aim to make diagnoses more accurate and improve patient results. Traditional techniques like dermoscopy and biopsy are still very important. However, newer technologies like multispectral images and computer-assisted analysis have made it much easier to tell the difference between normal and cancerous tumours early on. This review talks about how melanoma detection tools have changed over time and where they are now. It also talks about how artificial intelligence (AI) is being used in dermatology. Some new developments in high-resolution imaging, like confocal microscopy and optical coherence tomography, offer non-invasive options for deeper tissue analysis and real-time identification of cancerous cells, which can be very important for starting treatment early. Also, improvements in teledermatology have made it easier to do screenings from afar, making it easier for more people to get expert care and second views, which is especially helpful in areas that don't have enough resources. Melanoma identification has been changed forever by the use of deep learning models that can look at pictures of skin lesions with the same level of accuracy as doctors. These AI systems are trained on very large datasets and are being used more and more to help doctors make decisions, which could help cut down on medical mistakes and bias. Not only that, but AI-powered tools also show a lot of promise for keeping track of how lesions change over time, which is an important part of watching for melanoma. Also, genetic markers and biomarkers have become very useful for finding people who are at risk, which allows for proactive control and personalised treatment plans.

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