scholarly journals Robustness of convolutional neural networks in recognition of pigmented skin lesions

2021 ◽  
Vol 145 ◽  
pp. 81-91
Author(s):  
Roman C. Maron ◽  
Sarah Haggenmüller ◽  
Christof von Kalle ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

Skin Cancer Classification with Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 1707-1713 ◽  
Author(s):  
Mingang Chen ◽  
Wenjie Chen ◽  
Wei Chen ◽  
Lizhi Cai ◽  
Gang Chai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Survival Rates ◽  
Skin Lesions ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Image Preprocessing ◽  
Skin Cancers

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

scholarly journals Effect of image noise on the classification of skin lesions using deep convolutional neural networks

2020 ◽  
Vol 25 (3) ◽  
pp. 425-434 ◽  
Author(s):  
Xiaoyu Fan ◽  
Muzhi Dai ◽  
Chenxi Liu ◽  
Fan Wu ◽  
Xiangda Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Image Noise ◽  
Deep Convolutional Neural Networks

scholarly journals Skin Cancer Classification using Convolutional Neural Networks: Systematic Review (Preprint)

2018 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Jochen Sven Utikal ◽  
Dirk Schadendorf ◽  
Carola Berking ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Skin Lesions ◽  
Data Sets ◽  
Classification Methods ◽  
Public Data

BACKGROUND State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outside the hospital via installation on mobile devices. To our knowledge, at present, there is no review of the current work in this research area. OBJECTIVE This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. METHODS We searched the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. RESULTS We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large data set and then optimize its parameters to the classification of skin lesions are both the most common methods as well as display the best performance with the currently available limited data sets. CONCLUSIONS CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use non-public data sets for training and/or testing, thereby making reproducibility difficult.

scholarly journals The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 451
Author(s):  
Federica Veronese ◽  
Francesco Branciforti ◽  
Elisa Zavattaro ◽  
Vanessa Tarantino ◽  
Valentina Romano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Skin Lesions ◽  
Melanocytic Nevus ◽  
Ease Of Use ◽  
Small Data ◽  
Seborrheic Keratosis ◽  
Data Set ◽  
Face To Face

Background. The use of teledermatology has spread over the last years, especially during the recent SARS-Cov-2 pandemic. Teledermoscopy, an extension of teledermatology, consists of consulting dermoscopic images, also transmitted through smartphones, to remotely diagnose skin tumors or other dermatological diseases. The purpose of this work was to verify the diagnostic validity of images acquired with an inexpensive smartphone microscope (NurugoTM), employing convolutional neural networks (CNN) to classify malignant melanoma (MM), melanocytic nevus (MN), and seborrheic keratosis (SK). Methods. The CNN, trained with 600 dermatoscopic images from the ISIC (International Skin Imaging Collaboration) archive, was tested on three test sets: ISIC images, images acquired with the NurugoTM, and images acquired with a conventional dermatoscope. Results. The results obtained, although with some limitations due to the smartphone device and small data set, were encouraging, showing comparable results to the clinical dermatoscope and up to 80% accuracy (out of 10 images, two were misclassified) using the NurugoTM demonstrating how an amateur device can be used with reasonable levels of diagnostic accuracy. Conclusion. Considering the low cost and the ease of use, the NurugoTM device could be a useful tool for general practitioners (GPs) to perform the first triage of skin lesions, aiding the selection of lesions that require a face-to-face consultation with dermatologists.

scholarly journals Classification of Skin cancer using deep learning, Convolutional Neural Networks - Opportunities and vulnerabilities- A systematic Review

2020 ◽  
Vol 6 (11) ◽  
pp. 101-108
Author(s):  
Ravi Manne, Snigdha Kantheti and Sneha Kantheti
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Misclassification Rate ◽  
Research Articles ◽  
Original Research ◽  
Cancer Type

Background: Skin cancer classificationusing convolutional neural networks (CNNs) proved better results in classifying skin lesions compared with dermatologists which is lifesaving in terms of diagnosing. This will help people diagnosetheir cancer on their own by just installing app on mobile devices. It is estimated that 6.3 billion people will use the subscriptions by the end of year 2021[28] for diagnosing their skin cancer. Objective: This study represents review of many research articles on classifying skin lesions using CNNs. With the recent enhancement in machine learning algorithms, misclassification rate of skin lesions has reduced compared to a dermatologist classifying them.In this article we discuss how using CNNs has evolved in successfully classifying skin cancer type, and methods implemented, and the success rate. Even though Deep learning using CNN has advantages compared to a dermatologist, it also has some vulnerabilities, in terms of misclassifying images under some Criteria, and situations. We also discuss about those Vulnerabilities in this review study. Methods: We searched theScienceDirect, PubMed,Elsevier, Web of Science databases and Google Scholar for original research articles that are published. We selected papers that have sufficient data and information regarding their research, and we created a review on their approaches and methods they have used. From the articles we searched online So far no review paper has discussed both opportunities and vulnerabilities that existed in skin cancer classification using deep learning. Conclusions: The improvements in machine learning, Deep learning techniques, can avoid human mistakes that could be possible in misclassifying and diagnosing the disease. We will discuss, how Deep learning using CNN helped us and its vulnerabilities.

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