👍🏻Deep Learning helps you to be healthy!
For years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), which can be an indication of skin cancer. Early-stage identification of SPLs can improve melanoma prognosis and significantly reduce treatment cost. But it is not easy quickly find and prioritize SPLs due to the high volume of pigmented lesions. Researchers from MIT have devised a new AI pipeline, using deep convolutional neural networks (DCNNs) and applying them to analyzing SPLs through the wide-field photography common in smartphones.
A wide-field image, acquired with a smartphone camera, shows large skin sections from a patient. An automated system detects, extracts, and analyzes all pigmented skin lesions observable in the wide-field image. A pre-trained DCNN ML-models determines the suspiciousness of individual pigmented lesions and marks them: further inspection as yellow, referral to dermatologist as red. Extracted features are used to further assess pigmented lesions and to display results in a heatmap format.
DCNNs are deep learning algorithms are used to classify images to then cluster them for performing a photo search.
https://news.mit.edu/2021/artificial-intelligence-tool-can-help-detect-melanoma-0402