Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images

Ranpreet Kaur; Hamid GholamHosseini; Roopak Sinha; María Lindén

doi:10.1186/s12880-022-00829-y

ScienceGate Book Chapters

JOURNAL ARTICLE

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images

Ranpreet Kaur Hamid GholamHosseini Roopak Sinha María Lindén

Year: 2022 Journal: BMC Medical Imaging Vol: 22 (1)Pages: 103-103 Publisher: BioMed Central

DOI: 10.1186/s12880-022-00829-y

Get Full-Text PDF Get Analytical Report

Keywords:

Convolutional neural network Computer science Artificial intelligence Segmentation Deep learning Skin cancer Pattern recognition (psychology) Benchmark (surveying) Normalization (sociology) Artificial neural network Cancer Medicine Cartography

Metrics

Cited By

10.87

FWCI (Field Weighted Citation Impact)

Refs

0.98

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Cutaneous Melanoma Detection and Management

Health Sciences → Medicine → Oncology

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Nonmelanoma Skin Cancer Studies

Health Sciences → Medicine → Epidemiology

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images

Metrics

Citation History

Topics

Related Documents

Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer images

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images

Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer images

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network