Proceedings ArticleDOI
Skin lesion segmentation in clinical images using deep learning
Mohammad H. Jafari,Nader Karimi,Ebrahim Nasr-Esfahani,Shadrokh Samavi,S.M.R. Soroushmehr,Kevin R. Ward,Kayvan Najarian +6 more
- pp 337-342
TLDR
The experimental results show that the proposed method for accurate extraction of lesion region can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.Abstract:
Melanoma is the most aggressive form of skin cancer and is on rise. There exists a research trend for computerized analysis of suspicious skin lesions for malignancy using images captured by digital cameras. Analysis of these images is usually challenging due to existence of disturbing factors such as illumination variations and light reflections from skin surface. One important stage in diagnosis of melanoma is segmentation of lesion region from normal skin. In this paper, a method for accurate extraction of lesion region is proposed that is based on deep learning approaches. The input image, after being preprocessed to reduce noisy artifacts, is applied to a deep convolutional neural network (CNN). The CNN combines local and global contextual information and outputs a label for each pixel, producing a segmentation mask that shows the lesion region. This mask will be further refined by some post processing operations. The experimental results show that our proposed method can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.read more
Citations
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Journal ArticleDOI
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
Julia K. Winkler,Christine Fink,Ferdinand Toberer,Alexander Enk,Teresa Deinlein,Rainer Hofmann-Wellenhof,Luc Thomas,Aimilios Lallas,Andreas Blum,Wilhelm Stolz,Holger A. Haenssle +10 more
TL;DR: It is suggested that skin markings should be avoided in dermoscopic images intended for analysis by a CNN by increasing the melanoma probability scores and consequently the false-positive rate.
Journal ArticleDOI
An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models
TL;DR: A deep convolutional neural network model based on deep learning approach for the accurate classification between benign and malignant skin lesions is proposed and defined as more reliable and robust when compared with existing transfer learning models.
Journal ArticleDOI
A comparative study of deep learning architectures on melanoma detection.
TL;DR: This paper evaluates the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions in order to enhance the quality of images and apply data augmentation methodology to address the class skewness problem.
Journal ArticleDOI
Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework
TL;DR: A fully automated computerized aided diagnosis (CAD) system is proposed based on the deep learning framework and a fair comparison with other state-of-the-art is provided to further increase confidence in the proposed framework.
Journal ArticleDOI
Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma.
M. Hossein Jafari,Ebrahim Nasr-Esfahani,Nader Karimi,S. M. Reza Soroushmehr,Shadrokh Samavi,Shadrokh Samavi,Kayvan Najarian +6 more
TL;DR: A new method based on deep neural networks is proposed for accurate extraction of a lesion region and can outperform other state-of-the-art algorithms exist in the literature.
References
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Caffe: Convolutional Architecture for Fast Feature Embedding
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Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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Saliency detection by multi-context deep learning
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