scholarly journals Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1503 ◽  
Author(s):  
Emanuele Torti ◽  
Raquel Leon ◽  
Marco La Salvia ◽  
Giordana Florimbi ◽  
Beatriz Martinez-Vega ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Hyperspectral Imaging ◽  
Cancer Detection ◽  
Medical Practice ◽  
Skin Lesions ◽  
Hyperspectral Images ◽  
Parallel Pipeline ◽  
Many Core ◽  
Skin Cancer Detection

The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists’ expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s.

scholarly journals PRELIMINARY STUDY ON SKIN CANCER DETECTION IN SENCAR MICE USING MUELLER OPTICAL COHERENCE TOMOGRAPHY

2009 ◽  
Vol 02 (03) ◽  
pp. 289-294 ◽  
Author(s):  
MILOŠ TODOROVIĆ ◽  
SHULIANG JIAO ◽  
GEORGE STOICA ◽  
LIHONG V. WANG
Keyword(s):  
Optical Coherence Tomography ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Morphological Changes ◽  
Optical Coherence ◽  
Optical Scanning ◽  
Integrated Optical ◽  
Skin Cancer Detection ◽  
Continuous Source

We report on the use of a fiber-based Mueller-matrix optical coherence tomography (OCT) system with continuous source-polarization modulation for in vivo imaging of early stages of skin cancer in SENCAR mice. A homemade hand-held probe with integrated optical scanning and beam delivering optics was coupled in the sample arm. The OCT images show the morphological changes in skin resulting from pre-cancerous papilloma formations that are consistent with histology, thus demonstrating the system's potential for early skin cancer detection.

scholarly journals Training Primary Care Physicians in Dermoscopy for Skin Cancer Detection: a Scoping Review

2019 ◽  
Vol 35 (4) ◽  
pp. 643-650 ◽  
Author(s):  
Jonathan A. Fee ◽  
Finbar P. McGrady ◽  
Cliff Rosendahl ◽  
Nigel D. Hart
Keyword(s):  
Primary Care ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Primary Care Physicians ◽  
Training Programs ◽  
Meta Analysis ◽  
Skin Lesions ◽  
Evidence Base ◽  
Care Physician ◽  
Skin Cancer Detection

AbstractIn many countries, patients with concerning skin lesions will first consult a primary care physician (PCP). Dermoscopy has an evidence base supporting its use in primary care for skin cancer detection, but need for training has been cited as a key barrier to its use. How PCPs train to use dermoscopy is unclear. A scoping literature review was carried out to examine what is known from the published literature about PCP training in dermoscopy. The methodological steps taken in this review followed those described by Arksey and O’Malley, as revised by Levac et al. Four electronic databases were searched for evidence published up to June 2018. Sixteen articles were identified for analysis, all published since 2000. Ten training programs were identified all of which addressed dermoscopy of pigmented skin lesions, among other topics. Ten articles reported on a range of outcomes measured after training and showed generally positive results in terms of improved diagnostic performance, although no meta-analysis was conducted. However, it was unclear whether trained PCPs continued to use dermoscopy after training. Observational questionnaire data revealed that many PCPs use dermoscopy in practice without any formal training. The literature generally supports the use of dermoscopy by PCPs, but it is unclear whether current training leads to long-term change in PCPs’ use of dermoscopy in clinical practice. Understanding this problem, as well as exploring PCPs’ training needs, is essential to develop training programs that will facilitate the uptake and use of dermoscopy in primary care.

scholarly journals Real-Time Mobile-Phone-Aided Melanoma Skin Lesion Detection Using Triangulation Technique

2020 ◽  
Vol 11 (3) ◽  
pp. 9-31
Author(s):  
Kumud Tiwari ◽  
Sachin Kumar ◽  
R. K. Tiwari
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Cancer Detection ◽  
Mobile Application ◽  
Laboratory Testing ◽  
Skin Lesions ◽  
Lesion Detection ◽  
Detection Accuracy ◽  
Skin Cancer Detection ◽  
Melanoma Skin

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas to improve the patient prognosis. A biopsy is a traditional method which is painful and invasive when used for skin cancer detection. This method requires laboratory testing which is not very efficient and time-consuming to detect skin lesions. To solve the above issue, a computer aided diagnosis (CAD) for skin lesion detection is needed. In this article, we have developed a mobile application with the capabilities to segment skin lesions in dermoscopy images using a triangulation method and categorize them into malignant or bengin lesions through a supervised method which is convolution neural network (CNN). This mobile application will make the skin cancer detection non-invasive which does not require any laboratory testing, making the detection less time consuming and inexpensive with a detection accuracy of 81%.

Melanoma Skin Cancer Detection based on Image Processing

2020 ◽  
Vol 16 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Nadia Smaoui Zghal ◽  
Nabil Derbel
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Early Stage ◽  
Skin Lesions ◽  
Simple Method ◽  
Common Cause ◽  
Extraction Stage ◽  
Skin Cancer Detection ◽  
Aided Diagnosis ◽  
Melanoma Skin

Background: Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient’s survival likelihood. Aims: This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules. Methods: The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer. Results and Conclusion: Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.

Imaging obliquely illuminated skin lesions for skin cancer detection

2000 ◽  
Author(s):  
Guillermo Marquez ◽  
Lihong V. Wang ◽  
Mehrube Mehrubeoglu ◽  
Nasser Kehtarnavaz
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Skin Lesions ◽  
Skin Cancer Detection
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