Science 3 min read

Deep Learning Network can Diagnose Skin Cancer Better Than Dermatologists

Africa Studio /

Africa Studio /

Researchers just developed a new deep learning network that can reportedly detect skin cancer better than dermatologists.

Using a type of artificial intelligence known as a deep learning convolutional neural network (CNN), researchers designed a CNN that is reportedly better at detecting skin cancer than qualified dermatologists.

An international team of researchers from the United States, France, and Germany reportedly taught an AI to determine the difference between dangerous skin lesions and benign ones using over 100,000 images. The deep learning network was then presented with malignant melanomas and benign moles, testing its capability against 58 dermatologists from 17 countries.

More than half of the dermatologists were considered experts with around five years worth of experience. About 19% had their years of experience ranging from two to five years while the remaining 29% were new doctors with less than two years of working experience in the field.

“The CNN works like the brain of a child. To train it, we showed the CNN more than 100,000 images of malignant and benign skin cancers and moles and indicated the diagnosis for each image. Only dermoscopic images were used, that is lesions that were imaged at a 10-fold magnification. With each training image, the CNN improved its ability to differentiate between benign and malignant lesions,” Professor Holger Haenssle, first author of the study from the University of Heidelberg in Germany, explained.

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After training the neural network, the researchers made two test sets of images from the library of Heidelberg that were never used in any training, making it unknown to the CNN.

The results of the test showed that the CNN was able to accurately detect 95% of the skin cancers from the images while the dermatologists were only able to identify around 86.6% correctly.

“Most dermatologists were outperformed by the CNN,” the researchers wrote in their paper published in the journal Annals of Oncology.

“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists, and it misdiagnosed fewer benign moles as malignant melanoma, which means it had a higher specificity; this would result in less unnecessary surgery.”

A CNN is an artificial neural network that is inspired by the biological processes of the human brain’s nerve cells. This deep learning system is said to be capable of learning by “looking” at images and teaching itself what it has to know so it can gradually enhance its performance.

To date, most physicians involved in skin cancer screening and treatment use dermoscopy to image or store lesions for documentation and follow up.

According to the researchers, their deep learning network could one day help dermatologists in screening skin cancer and making the right decision to either biopsy a lesion or not.

Do you think neural networks like this will ever replace human doctors?

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Chelle is the Product Management Lead at INK. She's an experienced SEO professional as well as UX researcher and designer. She enjoys traveling and spending time anywhere near the sea with her family and friends.

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