Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning

2019 ◽  
pp. 211-219
Author(s):  
Simone Bonechi ◽  
Monica Bianchini ◽  
Pietro Bongini ◽  
Giorgio Ciano ◽  
Giorgia Giacomini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesions

scholarly journals Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6048
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Andrzej Brodzicki ◽  
Bill Cassidy ◽  
Connah Kendrick ◽  
Moi Hoon Yap
Keyword(s):  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
The Body ◽  
Anatomic Site ◽  
The Past ◽  
Depth Analysis ◽  
The Face

Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

Research Trends in Dermatologist Level Automatic Classification of Various Skin Lesions using Deep Learning

2020 ◽  
Vol 11 (2) ◽  
pp. 652
Author(s):  
P. Keerthana ◽  
P. SivaRanjani ◽  
G. Sree Gayathri Devi ◽  
S. Shanmugapriya ◽  
G.H. Sindhuja
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Skin Lesions ◽  
Research Trends

scholarly journals Classification of Automatic Skin Lesions from Dermoscopic Images Utilizing Deep Learning

2019 ◽  
Vol 2019 (1) ◽  
pp. 107-114 ◽  
Author(s):  
Pablo Minango ◽  
Yuzo Iano ◽  
Ana Carolina Borges Monteiro ◽  
Reinaldo Padilha França ◽  
Gabriel Gomes de Oliveira
Keyword(s):  
Deep Learning ◽  
Skin Lesions

scholarly journals Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Bin Zhang ◽  
Xue Zhou ◽  
Yichen Luo ◽  
Hao Zhang ◽  
Huayong Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Disease ◽  
Evaluation Criteria ◽  
Skin Lesions ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Challenges And Opportunities ◽  
The Status ◽  
New Research

AbstractDeep learning has become an extremely popular method in recent years, and can be a powerful tool in complex, prior-knowledge-required areas, especially in the field of biomedicine, which is now facing the problem of inadequate medical resources. The application of deep learning in disease diagnosis has become a new research topic in dermatology. This paper aims to provide a quick review of the classification of skin disease using deep learning to summarize the characteristics of skin lesions and the status of image technology. We study the characteristics of skin disease and review the research on skin disease classification using deep learning. We analyze these studies using datasets, data processing, classification models, and evaluation criteria. We summarize the development of this field, illustrate the key steps and influencing factors of dermatological diagnosis, and identify the challenges and opportunities at this stage. Our research confirms that a skin disease recognition method based on deep learning can be superior to professional dermatologists in specific scenarios and has broad research prospects.

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