AI can better detect skin cancer than dermatologists, shows study

CNN is a class of deep artificial neural networks inspired by biological processes in that the neurons in the brain are connected to each other and respond to what the eye sees. A study conducted by an global team of researchers pitted experienced dermatologists against a machine learning system, known as a deep learning convolutional neural network, or CNN, to see which was more effective at detecting malignant melanomas. In a technique known as machine learning, the device was able to quickly evaluate information researchers presented it, and improve its ability to pinpoint skin cancers.

On average, the dermatologists correctly detected around 86.6 percent of melanomas while the CNN identified 95 percent of them.

The incidence of malignant melanoma is increasing, with an estimated 232,000 new cases worldwide and around 55,500 deaths from the disease each year [3].

A group of scientists from Germany, USA and France have developed a system of artificial intelligence that can the photo, to distinguish melanoma from harmless moles, according to The Guardian.

Australian researchers have praised a computer that has been hailed for being better than an global team of specialists at detecting skin cancer.

Researchers were able to recruit 58 doctors from 17 countries.

More than 76,600 people in the USA are diagnosed with melanoma each year, and more than 9,300 die from it, early detection is key for successful treatment. It's better and faster diagnosis. In stage two, the dermatologists improved their score to 88.9% of malignant melanomas and 75.7% non-cancerous lesions.

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The CNN was trained on over 100,000 dermoscopic images. According to the results of the experiment, this computer is more accurate than dermatologists.

"This CNN may serve physicians involved in skin cancer screening as an aid in their decision whether to biopsy a lesion or not".

The researchers concluded the convolutional neural network missed fewer melanomas and misdiagnosed benign moles less frequently than the dermatologists.

"Today, nothing can replace a thorough clinical examination", stated Victoria Mar and Peter Soyer, two renowned Australian dermatology professors, in the report that accompanied the study. It will allow doctors to surgically remove them before it starts spreading.

It should be noted that melanomas in some parts of the body, including the fingers, toes, and scalp, are hard to photograph, and the CNN may have difficulty recognizing atypical lesions or ones that patients aren't aware they have.

[1] "Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists", by H.A. Haenssle et al.

That being said, these impressive results indicate that we're about to experience a paradigm shift, not only in dermatology but in just about every medical field, thanks to developments in artificial intelligence.

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