A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer. (September 2022)

• Record Type: Journal Article
• Authors: Srivastava, Varun; Kumar, Deepika; Roy, Sudipta
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Medical imaging -- Image retrieval -- Image classification -- Texture detection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108259
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A faster texture based feature vector and a deep learning based method for skin cancer classification. Use of a bigger neighbourhood (7 × 7) yields a more powerful feature vector with less noise. Thereby the results are better as compared to recent related texture based algorithms. Hybrid architecture with use of feature vectors along with a deep neural network reduce the time drastically as compared to the existing deep learning based models. An average increase in accuracy for two publicly available datasets is more than 10%. Abstract: 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. Graphical abstract: Image, graphical abstract