Proceedings ArticleDOI
Skin Lesions Identification Using Deep Convolutional Neural Network
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TLDR
This paper proposes a 5-layer Convolutional Neural Network for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer, and achieves almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set.Abstract:
Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.read more
Citations
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Journal ArticleDOI
Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures
TL;DR: The authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification and recommend several new approaches.
Journal ArticleDOI
Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM
Rabbia Mahum,Suliman Aladhadh +1 more
TL;DR: In this article , a novel and robust skin cancer detection model was proposed based on features fusion, which employed the benefits of both ML- and DL-based algorithms for the classification of skin cancer into malignant and benign.
Journal ArticleDOI
A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks
A Sankar Reddy,G. M. P. +1 more
TL;DR: An investigation into significant research articles on skin cancer diagnosis that have been published in reputable journals was carried out and demonstrated the ability of deep network topologies to segment and analyze skin cancer.
Proceedings ArticleDOI
Skin lesion classification using machine learning approach: A survey
TL;DR: A complete study of procedures for distinguishing skin diseases from a healthy skin is presented in this paper , which will help examiners in creating effective models that automatically identify diseased skin from healthy skin images.
Journal ArticleDOI
Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming
TL;DR: Wang et al. as mentioned in this paper developed a two-stage genetic programming (GP) method, where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators, such as multiplication in a wrapper approach to improve the classification performance.
References
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Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Proceedings ArticleDOI
Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Noel C. F. Codella,David A. Gutman,M. Emre Celebi,Brian Helba,Michael A. Marchetti,Stephen W. Dusza,Aadi Kalloo,Konstantinos Liopyris,Nabin K. Mishra,Harald Kittler,Allan C. Halpern +10 more
TL;DR: The most recent edition of the dermoscopic image analysis benchmark challenge as discussed by the authors was organized to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer.
Proceedings ArticleDOI
PH 2 - A dermoscopic image database for research and benchmarking
TL;DR: The PH2 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.
Proceedings ArticleDOI
Melanoma detection by analysis of clinical images using convolutional neural network
Ebrahim Nasr-Esfahani,Shadrokh Samavi,Nader Karimi,S.M.R. Soroushmehr,Mohammad H. Jafari,Kevin R. Ward,Kayvan Najarian +6 more
TL;DR: Experimental results show that the proposed method for detection of melanoma lesions is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.
Proceedings ArticleDOI
Skin Cancer Classification using Deep Learning and Transfer Learning
TL;DR: An automated skin lesion classification method using a pre-trained deep learning network and transfer learning are utilized and the classification rate of the proposed method outperformed the performance of the existing methods.
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