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An embedded novel compact feature profile image in speech signal for teledermoscopy system.
Health Inf Sci Syst. 2020 Dec; 8(1):23.HI

Abstract

Background and objectives

Teledermoscopy is a promising telemedicine service for remote diagnosis and treatment of skin diseases using dermoscopy images. It requires high quality transmission services, efficient utilization of channel bandwidth, effective storage, and security. Thus, this work develops an improved teledermoscopy system that guarantees the efficient and secure transmission of the dermoscopy images. It proposed a novel feature-based secure diagnostic system that supports the automated classification of malignant melanoma and benign nevus at the receiver side (i.e. medical facility).

Methods

To overcome the transmission of the original dermoscopy images having large size, a novel representation of the dermoscopy images is proposed, namely the compact feature profile (CFP). The proposed CFP represents the dermoscopy image only using its significant features. For security purpose, the CFP is embedded as a watermark in a speech signal using singular value decomposition (SVD) watermarking at the transmitter. Then, the de-embedding/reconstruction process is performed at the receiver end using a proposed modified SVD technique. Finally, the extracted CFP is fed into a classifier for diagnosis at the receiver. To evaluate the robustness of the proposed system, an additive white Gaussian noise (AWGN) attack was employed during the transmission process. To improve the immunity against the AWGN attack, a novel speech signal weight factor is proposed at the watermarking process. Moreover, a compensation factor is calculated at the training phase to compensate the effect of the channel AWGN attack at the receiver. In addition, the superior transform domain and embedding positions of the CFP in the speech signal were studied.

Results

The experimental results established that the proposed CFP diagnostic system achieved high classification accuracy, sensitivity, specificity, and F-measure for classifying the two skin cancer classes with the presence of signal-to-noise ratio (SNR) ranging from 10 to 25 dB.

Conclusion

This work established that the newly proposed CFP watermarked in speech signal using the DWT-based modified SVD followed by single-level decomposition Db1 with hard thresholding wavelet denoising achieved efficient diagnostic teledermoscopy system.

Authors+Show Affiliations

Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA.Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32626574

Citation

Ashour, Amira S., et al. "An Embedded Novel Compact Feature Profile Image in Speech Signal for Teledermoscopy System." Health Information Science and Systems, vol. 8, no. 1, 2020, p. 23.
Ashour AS, Wahba MA, El-Wahab BSA, et al. An embedded novel compact feature profile image in speech signal for teledermoscopy system. Health information science and systems. 2020;8(1):23.
Ashour, A. S., Wahba, M. A., El-Wahab, B. S. A., Guo, Y., & Hawas, A. R. (2020). An embedded novel compact feature profile image in speech signal for teledermoscopy system. Health Information Science and Systems, 8(1), 23. https://doi.org/10.1007/s13755-020-00113-9
Ashour AS, et al. An Embedded Novel Compact Feature Profile Image in Speech Signal for Teledermoscopy System. Health information science and systems. 2020;8(1):23. PubMed PMID: 32626574.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An embedded novel compact feature profile image in speech signal for teledermoscopy system. AU - Ashour,Amira S, AU - Wahba,Maram A, AU - El-Wahab,Basant S Abd, AU - Guo,Yanhui, AU - Hawas,Ahmed Refaat, Y1 - 2020/06/25/ PY - 2019/11/04/received PY - 2020/06/19/accepted PY - 2021/12/01/pmc-release PY - 2020/7/7/entrez PY - 2020/7/7/pubmed PY - 2020/7/7/medline KW - Classification KW - Dermoscopy KW - Security KW - Segmentation KW - Singular value decomposition KW - Telemedicine KW - Watermarking SP - 23 EP - 23 JF - Health information science and systems VL - 8 IS - 1 N2 - Background and objectives: Teledermoscopy is a promising telemedicine service for remote diagnosis and treatment of skin diseases using dermoscopy images. It requires high quality transmission services, efficient utilization of channel bandwidth, effective storage, and security. Thus, this work develops an improved teledermoscopy system that guarantees the efficient and secure transmission of the dermoscopy images. It proposed a novel feature-based secure diagnostic system that supports the automated classification of malignant melanoma and benign nevus at the receiver side (i.e. medical facility). Methods: To overcome the transmission of the original dermoscopy images having large size, a novel representation of the dermoscopy images is proposed, namely the compact feature profile (CFP). The proposed CFP represents the dermoscopy image only using its significant features. For security purpose, the CFP is embedded as a watermark in a speech signal using singular value decomposition (SVD) watermarking at the transmitter. Then, the de-embedding/reconstruction process is performed at the receiver end using a proposed modified SVD technique. Finally, the extracted CFP is fed into a classifier for diagnosis at the receiver. To evaluate the robustness of the proposed system, an additive white Gaussian noise (AWGN) attack was employed during the transmission process. To improve the immunity against the AWGN attack, a novel speech signal weight factor is proposed at the watermarking process. Moreover, a compensation factor is calculated at the training phase to compensate the effect of the channel AWGN attack at the receiver. In addition, the superior transform domain and embedding positions of the CFP in the speech signal were studied. Results: The experimental results established that the proposed CFP diagnostic system achieved high classification accuracy, sensitivity, specificity, and F-measure for classifying the two skin cancer classes with the presence of signal-to-noise ratio (SNR) ranging from 10 to 25 dB. Conclusion: This work established that the newly proposed CFP watermarked in speech signal using the DWT-based modified SVD followed by single-level decomposition Db1 with hard thresholding wavelet denoising achieved efficient diagnostic teledermoscopy system. SN - 2047-2501 UR - https://www.unboundmedicine.com/medline/citation/32626574/An_embedded_novel_compact_feature_profile_image_in_speech_signal_for_teledermoscopy_system_ L2 - https://dx.doi.org/10.1007/s13755-020-00113-9 DB - PRIME DP - Unbound Medicine ER -
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