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Skin cancer is one of the most common cancer types affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, AI tools such as shallow and deep machine learning are used to aid in diagnosing skin cancer. Skin cancer is detected using computer algorithms and deep neural networks.
According to MIT news, melanoma is a type of malignant tumour responsible for more than 70 per cent of skin-cancer deaths worldwide. For approximately four years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), indicating skin cancer. Such early-stage identification of SPLs in primary care settings can improve melanoma prognosis and significantly reduce treatment costs.
However, due to the high volume of pigmented lesions that often need to be evaluated for potential biopsies, the challenge is that quickly finding SPLs is difficult. Researchers from MIT and elsewhere have devised a new AI pipeline using deep convolutional neural networks and applying them to analyse SPLs through wide-field photography common in most smartphones and personal cameras.
DCNNs are neural networks that can be used to classify (or “name”) images to then cluster them (such as when performing a photo search). These ML algorithms belong to the subset of deep learning.
Cameras are used to take wide-field photographs of large areas of patients’ bodies. The program leverages DCNNs to quickly and effectively identify and screen for early-stage melanoma, according to Luis R. Soenksen, a postdoc and a medical device expert currently acting as MITs first Venture Builder in AI and Healthcare.
Soeksen conducted the research with MIT researchers, including MIT Institute for Medical Engineering and Science (IMES) faculty members Martha J. Gray, W. Kieckefer Professor of Health Sciences and Technology, professor of electrical engineering and computer science; and James J. Collins, Termeer Professor of Medial Engineering and Science and Biological Engineering.
Soeksen, in a recent paper, explained that the Early detection of SPLs can save lives; however, the current capacity of medical systems to provide comprehensive skin screening at scare is still lacking.
The paper describes the development of an SPL analysis system using DCNNs to more quickly and efficiently identify skin lesions requiring more investigation and screening than during routine primary care visits or by the patients themselves. The system utilised DCNNs to optimise the identification and classification of SPLs in wide-field images.
Using AI, the researchers trained the system using 20,388 wide-field images from 133 patients. The photos were taken with a variety of ordinary cameras that are readily available to consumers. Dermatologists working with the researchers visually classified the lesions in the images for comparison. They found that the system achieved more than 90.3 per cent sensitivity in distinguishing SPLs from non-suspicious lesions, skins and complex backgrounds by avoiding the need for time-consuming individual lesion imaging.
The research suggests the system leveraging computer vision and deep neural networks. Quantifying such common signs can achieve comparable accuracy to expert dermatologists. The study is expected to allow for more rapid and accurate assessments of SPLS and could lead to earlier treatment of melanoma.
Image source: Unsplash
Source: MIT News