65 research outputs found
A Computational Method for the Image Segmentation of Pigmented Skin Lesions
Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College
Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions
Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network
Skin lesion is a severe disease in world-wide extent. Early detection of
melanoma in dermoscopy images significantly increases the survival rate.
However, the accurate recognition of melanoma is extremely challenging due to
the following reasons, e.g. low contrast between lesions and skin, visual
similarity between melanoma and non-melanoma lesions, etc. Hence, reliable
automatic detection of skin tumors is very useful to increase the accuracy and
efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is
a challenge focusing on the automatic analysis of skin lesion. In this paper,
we proposed two deep learning methods to address all the three tasks announced
in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature
extraction (task 2) and lesion classification (task 3). A deep learning
framework consisting of two fully-convolutional residual networks (FCRN) is
proposed to simultaneously produce the segmentation result and the coarse
classification result. A lesion index calculation unit (LICU) is developed to
refine the coarse classification results by calculating the distance heat-map.
A straight-forward CNN is proposed for the dermoscopic feature extraction task.
To our best knowledges, we are not aware of any previous work proposed for this
task. The proposed deep learning frameworks were evaluated on the ISIC 2017
testing set. Experimental results show the promising accuracies of our
frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were
achieved.Comment: ISIC201
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.IngenierÃa de Sistemas Audiovisuale
A Review on Skin Disease Classification and Detection Using Deep Learning Techniques
Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches
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