Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems
Autor: | Francesco Leporati, Himar Fabelo, Giordana Florimbi, Samuel Ortega, Emanuele Torti, Marco La Salvia, Beatriz Martinez-Vega, Raquel Leon, Gustavo M. Callico |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Computer Networks and Communications
Computer science hyperspectral imaging lcsh:TK7800-8360 multicore CPU 01 natural sciences 010309 optics Lesion 03 medical and health sciences 0302 clinical medicine 0103 physical sciences Biopsy medicine graphic processing units Electrical and Electronic Engineering skin and connective tissue diseases Pixel medicine.diagnostic_test business.industry lcsh:Electronics Hyperspectral imaging Pattern recognition medicine.disease Pipeline (software) cancer detection real-time systems Hardware and Architecture Control and Systems Engineering 030220 oncology & carcinogenesis Hybrid system Signal Processing Artificial intelligence Skin cancer medicine.symptom Pigmented skin business |
Zdroj: | Electronics Volume 9 Issue 9 Electronics, Vol 9, Iss 1503, p 1503 (2020) |
ISSN: | 2079-9292 |
DOI: | 10.3390/electronics9091503 |
Popis: | 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&rsquo 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. |
Databáze: | OpenAIRE |
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