Journal ArticleDOI
Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network
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TLDR
A deep depthwise separable residual convolutional algorithm is introduced to perform binary melanoma classification on a dermoscopic skin lesion image dataset and dynamic effectiveness of the model is shown through its performance in multiple skin lesions image datasets.Abstract:
Melanoma is one of the four major types of skin cancers caused by malignant growth in the melanocyte cells. It is the rarest one, accounting to only 1% of all skin cancer cases. However, it is the deadliest among all the skin cancer types. Owing to its rarity, efficient diagnosis of the disease becomes rather difficult. Here, a deep depthwise separable residual convolutional algorithm is introduced to perform binary melanoma classification on a dermoscopic skin lesion image dataset. Prior to training the model with the dataset noise removal from the images using non-local means filter is performed followed by enhancement using contrast-limited adaptive histogram equilisation over discrete wavelet transform algorithm. Images are fed to the model as multi-channel image matrices with channels chosen across multiple color spaces based on their ability to optimize the performance of the model. Proper lesion detection and classification ability of the model are tested by monitoring the gradient weighted class activation maps and saliency maps, respectively. Dynamic effectiveness of the model is shown through its performance in multiple skin lesion image datasets. The proposed model achieved an ACC of 99.50% on international skin imaging collaboration (ISIC), 96.77% on PH2, 94.44% on DermIS and 95.23% on MED-NODE datasets.read more
Citations
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Journal ArticleDOI
Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review
TL;DR: In this article, the authors reviewed, synthesized and evaluated the quality of evidence for the diagnostic accuracy of computer-aided systems for skin lesion diagnosis, including 53 articles using traditional machine learning methods and 49 articles using deep learning methods.
Journal ArticleDOI
Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities
TL;DR: This research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets and proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma Detection solutions.
Journal ArticleDOI
Deep Learning in Skin Disease Image Recognition: A Review
TL;DR: The results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.
Journal ArticleDOI
W-net and inception residual network for skin lesion segmentation and classification
TL;DR: A new deep learning system for melanoma detection that consists of three steps: pre-processing, segmentation and classification, and the comparison of the proposed approach with other related work confirmed the advantages of the technique.
Journal ArticleDOI
An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection
TL;DR: An explainability method is developed by shapely adaptive explanations to produce heatmaps that visualize the areas of melanoma images that are most indicative of the disease that provide interpretability of the model’s decision in a manner understandable to dermatologists.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
Xception: Deep Learning with Depthwise Separable Convolutions
TL;DR: This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.
Posted Content
Network In Network
Min Lin,Qiang Chen,Shuicheng Yan +2 more
TL;DR: With enhanced local modeling via the micro network, the proposed deep network structure NIN is able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.
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.
Journal ArticleDOI
Deep learning ensembles for melanoma recognition in dermoscopy images
Noel C. F. Codella,Quoc-Bao Nguyen,Sharathchandra U. Pankanti,David A. Gutman,Brian Helba,Allan C. Halpern,John R. Smith +6 more
TL;DR: A system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection is proposed.