![]() Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. ![]() Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.Ĭancer originates from the uncontrolled growth of healthy cells into a mass. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. The choice of preprocessing scheme influenced the performance of the system. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. Most reports implemented simple image processing or machine learning, due to small training datasets. HMSI-based frameworks for skin tissue classification and segmentation vary greatly. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.Ī systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. Skin cancer is one of the most prevalent cancers worldwide. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. ![]() This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion.
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