Effect of Image Downsizing and Color Reduction on Skin Cancer Pre-screening

Setiawan, Andri; Amir, Faisal; Resfita, Nova

doi:10.1109/isitia49792.2020.9163734

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…Younis et al [11] made skin cancer was classified through employed deep learning, which introduced transfer learning and adjusted the MobileNet convolution neural network that trained in advance. Setiawan et al [12] explored the influence of skin cancer image reduction and color dimensionality reduction on skin cancer detection, and the above influencing factors were classified by K-means clustering. Meanwhile, Sabri et al [13] proposed the modified integrate learning strategy to classify skin cancer, which through extracting the shape, color and texture features of the lesion area, different machine learning methods were employed to classify these features.…”

Section: Introductionmentioning

confidence: 99%

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

2022

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Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

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Section: Introductionmentioning

confidence: 99%