Intelligent fusion-assisted skin lesion localization and classification for smart healthcare

2021 ◽  
Author(s):  
Muhammad Attique Khan ◽  
Khan Muhammad ◽  
Muhammad Sharif ◽  
Tallha Akram ◽  
Seifedine Kadry
Keyword(s):  
Skin Lesion ◽  
Smart Healthcare ◽  
Lesion Localization

A simple method for skin/lesion localization using magnetic resonance imaging

1994 ◽  
Vol 67 (800) ◽  
pp. 813-815 ◽  
Author(s):  
P T English ◽  
C Dougal ◽  
P D Griffiths ◽  
A Gholkar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Skin Lesion ◽  
Resonance Imaging ◽  
Simple Method ◽  
Lesion Localization

scholarly journals Deep metric attention learning for skin lesion classification in dermoscopy images

2022 ◽  
Author(s):  
Xiaoyu He ◽  
Yong Wang ◽  
Shuang Zhao ◽  
Chunli Yao
Keyword(s):  
Skin Lesion ◽  
Metric Learning ◽  
Feature Representation ◽  
Training Procedure ◽  
Training Samples ◽  
Triplet Loss ◽  
Deep Metric Learning ◽  
Localization Ability ◽  
Lesion Localization ◽  
Lesion Classification

AbstractCurrently, convolutional neural networks (CNNs) have made remarkable achievements in skin lesion classification because of their end-to-end feature representation abilities. However, precise skin lesion classification is still challenging because of the following three issues: (1) insufficient training samples, (2) inter-class similarities and intra-class variations, and (3) lack of the ability to focus on discriminative skin lesion parts. To address these issues, we propose a deep metric attention learning CNN (DeMAL-CNN) for skin lesion classification. In DeMAL-CNN, a triplet-based network (TPN) is first designed based on deep metric learning, which consists of three weight-shared embedding extraction networks. TPN adopts a triplet of samples as input and uses the triplet loss to optimize the embeddings, which can not only increase the number of training samples, but also learn the embeddings robust to inter-class similarities and intra-class variations. In addition, a mixed attention mechanism considering both the spatial-wise and channel-wise attention information is designed and integrated into the construction of each embedding extraction network, which can further strengthen the skin lesion localization ability of DeMAL-CNN. After extracting the embeddings, three weight-shared classification layers are used to generate the final predictions. In the training procedure, we combine the triplet loss with the classification loss as a hybrid loss to train DeMAL-CNN. We compare DeMAL-CNN with the baseline method, attention methods, advanced challenge methods, and state-of-the-art skin lesion classification methods on the ISIC 2016 and ISIC 2017 datasets, and test its generalization ability on the PH2 dataset. The results demonstrate its effectiveness.

Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification

2021 ◽  
Vol 90 ◽  
pp. 106956
Author(s):  
Muhammad Attique Khan ◽  
Yu-Dong Zhang ◽  
Muhammad Sharif ◽  
Tallha Akram
Keyword(s):  
Skin Lesion ◽  
Learning Framework ◽  
Lesion Localization

The use of Integra dermal regeneration matrix in the surgical treatment of a recurrent, extensive malignant skin lesion in the temporal region

2013 ◽  
Vol 154 (6) ◽  
pp. 225-227 ◽  
Author(s):  
Csaba Halmy ◽  
Zoltán Nádai ◽  
Krisztián Csőre ◽  
Adrienne Vajda ◽  
Róbert Tamás
Keyword(s):  
Surgical Treatment ◽  
Skin Lesion ◽  
Surgical Management ◽  
Skin Grafting ◽  
Skin Tumor ◽  
Temporal Region ◽  
Aesthetic Result ◽  
Malignant Skin ◽  
Dermal Regeneration Template ◽  
Dermal Regeneration

Authors report on the use of Integra dermal regeneration template after excision of an extended, recurrent skin tumor in the temporal region. The area covered with Integra was 180 cm2. Skin grafting to cover Integra was performed on the 28th day. Both Integra and the skin transplant were taken 100%. Integra dermal regeneration template can provide good functional and aesthetic result in the surgical management of extended skin tumors over the skull. Orv. Hetil., 2013, 154, 225–227.

Security in Smart Healthcare System: A Comprehensive Survey

2017 ◽  
Vol 7 (10) ◽  
pp. 39
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Healthcare Systems ◽  
Internet Security ◽  
Security Risk ◽  
System A ◽  
Smart Healthcare ◽  
Comprehensive Survey ◽  
Healthcare Monitoring ◽  
The Internet Of Things

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.

Sign in / Sign up

Export Citation Format

Share Document