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Skin Lesion Detection using Texture Based Segmentation and Classification by Convolutional Neural Networks (CNN)
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Skin lesion segmentation from dermoscopic images is a very challenging task nowadays because of the contrast of those images. there are various techniques for detecting the skin cancer base on the characteristics ...
Nowadays Deep Learning technique is very popular for classification of images. CNN is one of the techniques of Deep Learning. The proposed work will help in classification of skin lesion. ...
Skin Lesion In the segmentation step, they applied Grab-cut algorithm and K-means algorithm is used for clustering and learned features from colour dermoscopic images dataset to improve the boundary of ...
doi:10.35940/ijitee.b7085.129219
fatcat:7idyakj7ireipb4orxua5xti6a
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
[article]
2017
arXiv
pre-print
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The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards ...
The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. ...
The segmentation of the lesion from dermoscopic images is an important aspect of melanoma detection, as some of the features which are used by clinicians in dermoscopy algorithms are based on the shape ...
arXiv:1703.01025v1
fatcat:sth5akf2hzfp5eopainabkvxsu
Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation
2021
Computer Optics
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In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. ...
Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. ...
To improve the diagnostic quality by ABCD rule, it is necessary to segment skin lesions from dermoscopic images. ...
doi:10.18287/2412-6179-co-748
fatcat:sljt6cepafgkbdf6asdpvssttq
Pattern Recognition in Macroscopic and Dermoscopic Images for Skin Lesion Diagnosis
[chapter]
2017
Lecture Notes in Computational Vision and Biomechanics
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Pattern recognition in macroscopic and dermoscopic images is a challenging task in skin lesion diagnosis. ...
Hence, this work was particularly focused on skin lesion pattern recognition, especially in macroscopic and dermoscopic images. ...
Ricardo Baccaro Rossetti, from Derm Clínica's Dermatologist of São José do Rio Preto, in Brazil, for his suggestions and for evaluating the results obtained. ...
doi:10.1007/978-3-319-68195-5_55
fatcat:tuhbeyqsrja35n25w5ivgabfte
SKIN LESION SEGMENTATION BY PIXEL BY PIXEL APPROACH USING DEEP LEARNING
2020
International journal of advances in signal and image sciences
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Skin lesion segmentation is an imperative step for image analysis and visualization task. ...
The PbP approach has four stages; study the training images consists of skin lesions, construction of deep learning network followed by training it and finally evaluate the network with testing images. ...
Optimal colour channel based skin lesion segmentation is discussed in [15] . Initially, the given dermoscopic images are resized uniformly and then de-noised. ...
doi:10.29284/ijasis.6.1.2020.12-20
fatcat:vnmrkrwpj5hl3dslra6cz6akxm
Multilevel feature extraction for skin lesion segmentation in dermoscopic images
2012
Medical Imaging 2012: Computer-Aided Diagnosis
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This paper presents a novel approach in computer aided skin lesion segmentation of dermoscopic images. We apply spatial and color features in order to model the lesion growth pattern. ...
A dataset containing 116 challenging images from dermoscopic atlases is used to validate the method via a 10-fold cross validation procedure. ...
walker algorithm 25 is also employed to segment the skin lesion in dermoscopic images. ...
doi:10.1117/12.911664
dblp:conf/micad/KhakAbiWLA12
fatcat:g2jym44dcvaclisi5okwrzgxl4
Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation
2022
Diagnostics
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Segmentation of skin lesions in dermoscopic images is an important requisite component of such a breakthrough innovation for an accurate melanoma diagnosis. ...
Novel image segmentation methods are aimed to address these undesirable heterogeneous properties of skin lesions with the help of image preprocessing methods. ...
clusters and efficacious segmentation of skin lesions in dermoscopic images ...
doi:10.3390/diagnostics12020344
pmid:35204435
pmcid:PMC8871329
fatcat:5l34hkerc5c6bdt3ucjn3v2s64
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
[article]
2020
arXiv
pre-print
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The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images. ...
Although deep learning-based approaches have considerably improved the segmentation accuracy, there is still room for improvement by addressing the major challenges, such as variations in lesion shape, ...
Recent years have witnessed the successful applications of machine learning, particularly deep learning-based approaches, to the semantic segmentation of skin lesions. ...
arXiv:2003.10111v1
fatcat:ruelnx63gbclvdp3ping7pgcfa
Guest Editorial Skin Lesion Image Analysis for Melanoma Detection
2019
IEEE journal of biomedical and health informatics
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ACKNOWLEDGMENT The guest editors thank all those who helped make this special issue possible, especially the Editor-in-Chief, editorial staff, and the authors and reviewers of the contributions.
M. ...
In Accurate Segmentation and Registration of Skin Lesion Images to Evaluate Lesion Change, Navarro et al. describe a superpixel based segmentation algorithm. ...
In Active Contours Based Segmentation and Lesion Periphery Analysis for Characterization of Skin Lesions in Dermoscopy Images, Riaz et al. propose an active contour based segmentation approach. ...
doi:10.1109/jbhi.2019.2897338
fatcat:3cszhtjw6zc5thl4abzeaukq3q
Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
2019
IEEE Transactions on Image Processing
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2020 pre-print arXiv:2002.08694v1 fatcat:ho7wcytd3ncrjhzd22muetvjai
Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. ...
It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion ...
ACKNOWLEDGMENT The authors would like to thank the organizers of International Symposium on Biomedical Imaing 2016 and 2017 for kindly providing benchmark databases and annotations. ...
doi:10.1109/tip.2019.2955297
fatcat:tclk22buhzdufnymgbt7tm5zpq
Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network
2018
Sensors
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Zhou developed several mean-shift-based approaches for segmenting skin lesions in dermoscopic images [14] [15] [16] . ...
In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction ...
of skin lesion image processing, i.e., lesion segmentation, dermoscopic feature extraction and lesion classification. ...
doi:10.3390/s18020556
pmid:29439500
pmcid:PMC5855504
fatcat:juvmkzufnjcybopxo6v4plppfy
Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images
2020
Journal of Imaging
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In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. ...
Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. ...
Illustrative examples of macroscopic (row above) and dermoscopic (below) skin lesions. ...
doi:10.3390/jimaging7010002
pmid:34460573
pmcid:PMC8321267
fatcat:zaj6phc4cjhrpfvuiox6o6qyti
A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification
2022
Journal of Healthcare Engineering
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The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction ...
As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning ...
the exact skin lesion region from the preprocessed dermoscopic images known as the region of lesion (ROL). ...
doi:10.1155/2022/1709842
pmid:35480147
pmcid:PMC9038388
fatcat:jy5k3tworncwhnna6xbethhp5u
PH2 - A dermoscopic image database for research and benchmarking
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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The PH 2 database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images ...
The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. ...
Marta Pereira, both Dermatologists of the Hospital Pedro Hispano, for evaluating the dermoscopic images. We also thank to Catarina Barata and Bárbara Amorim from ADDI project. ...
doi:10.1109/embc.2013.6610779
pmid:24110966
dblp:conf/embc/MendoncaFMMR13
fatcat:aejlfc4gircffm6zt3gkuoccoy
(De)Constructing Bias on Skin Lesion Datasets
[article]
2019
arXiv
pre-print
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Nowadays, the ISIC Archive and the Atlas of Dermoscopy dataset are the most employed skin lesion sources to benchmark deep-learning based tools. ...
Our results show that models can correctly classify skin lesion images without clinically-meaningful information: disturbingly, the machine-learning model learned over images where no information about ...
When exploring reproducible works on lesion segmentation [8, 24] , dermoscopic attribute segmentation [14] , skin lesion classification [7, 19, 23] , or skin lesion synthesis [6] those two datasets ...
arXiv:1904.08818v1
fatcat:smsadebuf5bfvfve5tjdqu2n2e
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