167 research outputs found
Computer aided diagnosis system using dermatoscopical image
Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert
dermatologist decision when watching a dermoscopic or clinical image. Computer Vision
techniques, which can be based on expert knowledge or not, are used to characterize the
lesion image. This information is delivered to a machine learning algorithm, which gives a
diagnosis suggestion as an output.
This research is included into this field, and addresses the objective of implementing a
complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input.
Some of them are based on expert knowledge and others are typical in a wide variety of
problems. Images are initially transformed into oRGB, a perceptual color space, looking for
both enhancing the information that images provide and giving human perception to machine
algorithms. Feature selection is also performed to find features that really contribute to
discriminate between benign and malignant pigmented skin lesions (PSL). The problem of
robust model fitting versus statistically significant system evaluation is critical when working
with small datasets, which is indeed the case. This topic is not generally considered in works
related to PSLs. Consequently, a method that optimizes the compromise between these two
goals is proposed, giving non-overfitted models and statistically significant measures of
performance. In this manner, different systems can be compared in a fairer way. A database
which enjoys wide international acceptance among dermatologists is used for the
experiments.Ingeniería de Sistemas Audiovisuale
Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches
The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking.
To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts
Dermoscopy in the era of dermato-oncology: from bed to bench side and retour
Today dermoscopy is standard-of-care in the diagnosis and management of patients with benign and malignant skin tumors because it increases the diagnostic accuracy of skin lesions compared to the naked-eye examination up to 25%. Despite its role in the routine dermato-oncology, it increasingly gained interest as a bridge connecting clinical with basic molecular research in dermato-oncology. Here, we correlate dermoscopy patterns of nevi and melanomas with high and low susceptibility genes and somatic mutations, provide an overview on the clinical and dermoscopic patterns of cutaneous melanoma subtypes, and highlight the role of dermoscopy in the diagnosis of skin eruptions during systemic treatments of advanced melanoma including targeted therapies and immunotherapies
Automating the ABCD Rule for Melanoma Detection: A Survey
The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule
Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules.
OBJECTIVES: Malignant melanoma has high morbidity and mortality rates. Early diagnosis improves prognosis. Clinical prediction rules (CPRs) can be used to stratify patients with symptoms of suspected malignant melanoma to improve early diagnosis. We conducted a systematic review of CPRs for melanoma diagnosis in ambulatory care.
DESIGN: Systematic review.
DATA SOURCES: A comprehensive search of PubMed, EMBASE, PROSPERO, CINAHL, the Cochrane Library and SCOPUS was conducted in May 2015, using combinations of keywords and medical subject headings (MeSH) terms.
STUDY SELECTION AND DATA EXTRACTION: Studies deriving and validating, validating or assessing the impact of a CPR for predicting melanoma diagnosis in ambulatory care were included. Data extraction and methodological quality assessment were guided by the CHARMS checklist.
RESULTS: From 16 334 studies reviewed, 51 were included, validating the performance of 24 unique CPRs. Three impact analysis studies were identified. Five studies were set in primary care. The most commonly evaluated CPRs were the ABCD, more than one or uneven distribution of Colour, or a large (greater than 6 mm) Diameter (ABCD) dermoscopy rule (at a cut-point of \u3e4.75; 8 studies; pooled sensitivity 0.85, 95% CI 0.73 to 0.93, specificity 0.72, 95% CI 0.65 to 0.78) and the 7-point dermoscopy checklist (at a cut-point of ≥1 recommending ruling in melanoma; 11 studies; pooled sensitivity 0.77, 95% CI 0.61 to 0.88, specificity 0.80, 95% CI 0.59 to 0.92). The methodological quality of studies varied.
CONCLUSIONS: At their recommended cut-points, the ABCD dermoscopy rule is more useful for ruling out melanoma than the 7-point dermoscopy checklist. A focus on impact analysis will help translate melanoma risk prediction rules into useful tools for clinical practice
Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma
Melanoma is the most widely occurring and life threatening form
of skin cancer. Early detection of in situ melanoma has
challenged researchers for many decades now. Currently there
exists no computer aided mechanisms to accurately detect early
melanoma. T
he currently existing computer aided diagnostics
mechanisms are capable of melanoma classification and are
unable to detect in situ melanoma. This paper introduces a Multi
Parameter Extraction and Classification System (
푀푀푀푀푀
) to aid
early detection o
f skin cancer melanoma. The
푀푀푀푀푀
defines
the skin lesion images in terms of characteristic parameters
which are further used for classification. In this paper the
extraction of 21 parameters is achieved using a six phase
approach. The parameters extr
acted are analyzed using statistical
methods. It is clear from the results obtained that no single
parameter can affirm the detection of in situ melanoma, hence
an advanced analysis mechanisms considering all the parameters
need to be adopted to effective
ly detect melanoma in its initial
stages
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