Skin Cancer Detection Using Convolutional Neural Network

Deep Learning in Skin Disease Image Recognition: A Review

“…One-third of the collected literature use the mixture of multiple datasets [38], [42], [43], [46], [47], [49], [51], [52], [53], [58], [73], [76], [80], [85]. [49], [53], [54], [56], [57], [66], [71], [76], [79], [84], and [85] ISIC Archive [30] 23906 7 √ [46], [58], [59], [70], [77], [79], [80], and [81] DermIS [32] --× [46], [51], [58], and [80] DermQuest [33] --× [46], [51], and [80] DermNZ [34] --× [46] and [51] ISIC 2016 [35] 1279 2 √ [47], [52], …”

“…Table III shows the details. [47], [50], [51], [52], [53], [58], and [65] Clipping [38], [44], [49], [53], [55], [56], [57], and [75] - [41], [42], [59], and [70] Normalization Data normalization [41], [47], [48], [52], [53], [54], [56], [58], [65], [70], and [75] This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

“…Content may change prior to final publication. Grayscale conversion Converts the original color image to a grayscale image [47], [57], and [70] Size adjustment: its input size limits the network model of deep learning, and the size of its input image is mostly fixed. If the input size in the network model is large, then the abstract level of information cannot meet the needs of the network, and the amount of calculation will increase; if the input size is small, then the picture information will be lost; hence, the network model in deep learning mostly uses 224 × 224 or 227 × 227 fixed-size input.…”

“…Grayscale conversion: gray conversion operates on a single pixel of an image; its primary purpose is to improve the contrast of the image and threshold processing. In the collected literature, Mahbod et al [47], Singhal et al [57], and Hasan et al [70] used grayscale transformation to process skin disease pictures.…”

“…Twenty-seven pieces of literature used a single model in the literature, among which 10 papers ( [38], [38], [43], [57], [59], [64], [73], [75], [76], and [77]) has no model improvement, and the other 15 articles have optimized the model. The optimization methods mainly include: modifying the fully connected layer (five articles: [41], [44], [61], [74], and [80]), using a machine learning classifier (two articles: [65] and [81]), applying (1 article: [51]) the model to the mobile application, introducing attention mechanism (two articles: [50] and [55]), self-building network model (two articles: [58] and [70]), and multilevel classification (three articles: [46], [56], and [78]). Kawahara et al [41] and Yuexiang et al [44] formed a fully CNN based on CNNs and removed the fully connected layers.…”