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Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
تشخیص سرطان پوست با الگوریتم های یادگیری عمیق و آنالیز صدا: یک مطالعه بالینی آینده نگر از یک درموسکوپ ابتدایی-2019 Background: Skin cancer (SC), especiallymelanoma, is a growing public health burden. Experimental studies have
indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities.
Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional
sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine
the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier
with polarized light (SMP).
Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified.
Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity
and sensitivity,which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over
positive predictive values.
Findings: Patients (n=73) fulfilling inclusion criteriawere referred to biopsy. SMP analysis metrics resulted in a
receiver operator characteristic curve AUCs of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of
91.7%, specificity of 41.8% and positive predictive value of 57.3%.Diagnosing the sameset of patients lesions by an
advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive
value of 59.9% (P=NS).
Interpretation: DL processing of dermoscopic images followed by sonification results in an accurate diagnostic
output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of
skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system.
Fund: Bostel Technologies. Keywords: Skin cancer | Deep learning | Dermoscopy | Sonification | Melanoma | Telemedicine | Artificial intelligence |
مقاله انگلیسی |
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Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions
تجزیه و تحلیل داده های طیف سنجی رامان با الگوریتم های مبتنی بر منطق paraconsistent برای توصیف ضایعات سرطان پوست-2019 Analysis of the Raman data to obtain results in discrimination models is usually done with multivariate statistics
based on principal component analysis (PCA). In this work, we present a technique based on a non-classical logic
called paraconsistent logic (PL). The aim of this work is to use computational procedures capable of generating
efficient expert systems to discriminate cutaneous tissue samples obtained by Raman spectroscopy. First, a set of
algorithms originating from PL is presented, and then its application in discrimination analyses is described; the
discrimination analysis was conducted using a database of skin tissue samples obtained ex vivo by Raman
spectroscopy of spectrum range of 400–1800 cm−1 wavelengths. Data processing, pattern creation, and comparisons
were performed using the set of paraconsistent algorithms (SPA-PAL2v). The total number of samples
was divided into four histopathological groups, with 115 spectra of basal cell carcinoma (BCC), 21 spectra of
squamous cell carcinoma (SCC), 57 spectra of actinic keratosis (AK), and 30 normal skin (NO) spectra. An
arrangement type was created for this study, and the samples were randomly selected and analyzed, and the NO
group was compared with the group of non-melanoma cancer lesions (BCC+SCC) and the AK tumor lesion. Two
analyses were performed. The first (SPA-PAL2v) Mode 1 (no cross-validation) achieved 76% of hits, and the
second (SPA-PAL2v) Mode 2 (with cross-validation) achieved 75.78% of hits. These results were compared with
discrimination using PCA statistical methods (PCA/DA) and presented superior percentages of hits, which proves
the robustness of the SPA-PAL2v, confirming its potential for Raman spectrum data analysis. Keywords: Raman spectroscopy | Algorithms | Skin cancer | Paraconsistent annotated logic | Medical diagnosis |
مقاله انگلیسی |