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Full-Text Articles in Medicine and Health Sciences
Nanosecond Pulsed Electric Field (Nspef) Ablation As An Alternative Or Adjunct To Surgery For Treatment Of Cancer, Ru Chen, Xinhua Chen, Stephen J. Beebe
Nanosecond Pulsed Electric Field (Nspef) Ablation As An Alternative Or Adjunct To Surgery For Treatment Of Cancer, Ru Chen, Xinhua Chen, Stephen J. Beebe
Bioelectrics Publications
Surgery as resection or transplantation remains a fundamental means for cancer treatment and often offers an opportunity for a cure. However, surgery is not always possible because of tumor proximity to blood vessels or ducts or when a patient is not healthy enough to undergo surgery. Application of nanosecond pulsed electric fields (nsPEFs) is a new approach to treat cancer using pulse power technology that was originally designed for military purposes. This novel approach deposits extremely short pulses of high power, low energy electric fields into malignant tissues using electrodes to encompass tumors. Pre-clinical studies show that treatments are effective …
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Electrical & Computer Engineering Faculty Publications
In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …