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Filtro per: Parola chiave Texture detection Soggetto Artificial Intelligence

1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

Descrizione:: Skin Cancer is one of the most widespread forms of cancer in the world which can be detected using dermatoscopic images. In this paper, a texture based feature extraction algorithm is presented for the classification of dermatoscopic images. A median based Local Ternary Pattern is extracted followed by the computation of local quantized ternary patterns. The feature set extracted is then classified using a modified convolutional neural network. The images used for the detection of multiple types of skin cancer are obtained from two publicly available datasets, HAM10000 and ISICUDA11. For the proposed technique, the average recall value, average precision and average accuracy is found to be 75.20%, 95.44% and 96% respectively. An average increase in accuracy for the proposed algorithm is up-to 50.6%, 24.1% and 4.7% over LTP, DLTerQEP and a DE ANN based algorithm respectively.
Parola chiave:: Medical imaging, Image classification, Image retrieval, and Texture detection
Soggetto:: Medical Imaging, Data Science, Radiodiagnosis, Computer Science, Artificial Intelligence, and Radiology
Creatore:: Sudipta Roy , Varun Srivastava, and Deepika Kumar
Collaboratore:: Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, India
Owner:: n.sakthivel@jioinstitute.edu.in
Editore:: Elsevier
luogo:: United States
Lingua:: English
Data caricata:: 07-02-2023
Data modificata:: 16-02-2023
data di creazione:: 01-09-2022
Rights Statement Tesim:: In Copyright
Resource Type:: Article
Identifier:: 10.1016/j.compeleceng.2022.108259

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1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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