This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.
Abstract-Extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression because it outperforms conventional artificial neural networks (ANN), and support vector machines (SVM) in some aspects. ELM provides a robust learning algorithm, free of local minima, without overfitting problems and less dependent on human intervention than the above methods. ELM is appropriate for the implementation of intelligent autonomous systems with real-time learning capability. Moreover, a number of complex industrial applications demanding a high performance solution could benefit from this approach.This work proposes the modelling of a real complex cogeneration plant with the aim of obtaining higher energy production with a lower cost (i.e. maximum energy efficiency) using ELM. The accuracy and training time of the ELM-based model are compared with the results obtained using BP-ANN and SVM. ELM training is significantly faster than SLFNs and SVM while preserving the same accuracy level.
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