Application of Basic Epidemiologic Principles and Electronic Health Records in a Deep Learning Prediction Model—Reply

Wang, Hsiao Han; Liang, Chia Wei; Li, Yu-Chuan

doi:10.1001/jamadermatol.2019.4440

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2021

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“…In the journal, a study performed for sorting of malignancy based on categorization using SVM and Convolutional Neural Network. This kind of malignancy diagnosing system, as recommended by [7]. There are six steps in a row.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Skin Cancer Prognosis Strategy Using Deep Learning Techniques

Sirisati¹,

Kumar²,

Latha³

2021

INDJCSE

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To start the right treatment, identification of an indefinite skin lesion is necessary. Only highly trained dermatologists who can treat them with an early diagnosis and diagnose melanoma skin lesions. The classification of skin for melanoma Dermoscopic images is 70 percent. Due to the limited supply of expertise, systems that sort dermal growth as the autoimmune or metastatic tumor can serve as an early screening tool. This study provides a model of the Convolutional neural network trained for skin lesion images, from previously acquired features of the Highway Convolutional neural network (CNN). It does not require advanced preprocessing. In addition, the model not require much computing power to train. The Convolutional neural network (CNN) method achieves training accuracy of 50%, and 70% of the test data have classification accuracy, low, moderate and high accuracy of the estimated damage.

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Section: Related Workmentioning

confidence: 99%

“…Characteristic mining is an essential pace in acquiring artificial intelligence. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Various techniques for removing various kinds of carcinoma. However, there are drawbacks to these techniques, depending on characteristic mining.…”

Section: Introductionmentioning

confidence: 99%