Open AccessSkin Cancer Classification Detection using CNN and SVM
Author(s) -
A. Pushpalatha,
Priyanka Dharani,
R Dharini,
J Gowsalya
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012148
Subject(s) - segmentation , skin cancer , computer science , artificial intelligence , convolutional neural network , skin lesion , melanoma , residual neural network , malignancy , deep learning , pattern recognition (psychology) , pooling , stage (stratigraphy) , cancer , dermatology , medicine , pathology , cancer research , biology , paleontology
Skin malignant growth is quite possibly the most commonly seen Malignancy type in people. Skin disease happens because of the un controllable developing of transformations occurring in DNAs developing to certain reasons. Perceiving the malignant growth in beginning phases could build the opportunity of an effective treatment. These days, PC helped finding applications are utilized nearly at each field. From the real dermo scopic images, the first-stage network aims for precise segmentation of the skin lesion. The second-stage network is a classification network that can predict the existence of Melanoma and Squamous Cell Carcinoma in a skin sample. Deep convolutional neural networks, such as Inception-v4, ResNet-152, and DenseNet-161, were trained for melanoma and squamous cell carcinoma detection and seborrheickeratosis classification. U-Net with VGG-16 Encoder was trained to create segmentation masks for lesion segmentation. Resnet engineering achieves the highest precision of 90 percent among the equations used in the proposed models.