Release date: 2018-05-30 Recently, researchers have shown for the first time that the deep learning convolutional neural network (CNN) has surpassed experienced dermatologists in detecting melanoma. The results of this study were published in the cancer journal Annals of Oncology. on. CNN is an artificial neural network and a form of artificial intelligence. It simulates the process by which nerve cells in the brain are connected to each other and to what is seen by the eye. CNN is able to quickly learn the images it “sees†and improve its performance during this learning process, reflecting the principles of machine learning. Convolutional Neural Network (CNN) is a form of artificial intelligence (Source: 123RF) In this study, the researchers used more than 100,000 skin cancer images and diagnostic results to train CNN to improve CNN's ability to differentiate between malignant and benign skin cancer. These images are dermoscopic images, that is, skin lesion images with a magnification of 10 times, including both benign and malignant skin cancers, as well as images of sputum on the skin. After completing the training, the researchers used 300 new images to assess CNN's ability to recognize cancer. At the same time, the researchers invited 58 dermatologists from 17 countries around the world to make a diagnosis based on dermoscopic images. The process is divided into two phases: in the first phase, doctors need to distinguish between malignant melanoma or benign sputum based on dermoscopic images and select measures to control the condition, including surgery, short-term follow-up, and no action required. In the second phase, after four weeks, the doctors will receive clinical information such as the patient's age, gender, and location, as well as close-up images of these patients, and make further diagnostic and follow-up decisions based on this information. The CNN test results (red curve) are compared with the average of dermatologist test results (large dark green dots). It can be seen that when the specificity (X-axis) values ​​are the same, the sensitivity of the CNN test results (Y-axis) is significantly higher than that of the dermatologist (Source: "Annals of Oncology") The results of the study showed that in the first phase, dermatologists were able to accurately detect an average of 86.6% of melanomas, while accurately identifying an average of 71.3% of non-malignant lesions. However, when the accuracy of CNN to identify benign sputum reached the same 71.3%, it detected melanoma with an accuracy of 95%! In the second phase, dermatologists improved their performance to accurately diagnose 88.9% of malignant melanoma and 75.7% of non-cancer lesions. "These findings suggest that deep learning convolutional neural networks perform better than experienced dermatologists in detecting melanoma," said the first author of the study, Holger, senior management physician, Department of Dermatology, University of Heidelberg, Germany Professor Hanenssle said: "CNN can help doctors conduct skin cancer screening to determine whether a biopsy of the lesion is needed. Currently, most dermatologists have begun using electronic dermatoscopy systems to convert lesions into image form and store them. Convenient for recording and follow-up. CNN can quickly evaluate stored images for information on melanoma diagnosis. We are currently planning a prospective study to assess the actual impact of CNN on doctors and patients." Source: Sina Pharmaceutical News Manual Massage Collapsible Foot Spa Massager The Manual Massage Foldable Foot Spa Massager is a portable compact device designed to give your feet a relaxing massage experience. Its foldable design makes it easy to store and transport, making it ideal for the frequent traveler.
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