Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques

López, Adrià Romero; Giró-i-Nieto, Xavier; Burdick, Jack; Marques, Oge

doi:10.2316/p.2017.852-053

Cited by 143 publications

(64 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is the reason why this model was considered to perform the learning transfer. Lesion recognition systems using learning transfer have been described in the literature with the AlexNet [11], VGGNet [13,14], and ResNet [12,[15][16][17][18] network models, whose performance is similar to the model used for this work.…”

Section: Discussionmentioning

confidence: 99%

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

View full text Add to dashboard Cite

Image recognition and processing is a suitable tool in systems using machine learning methods. The addition of smartphones as complementary tools in the health area for diagnosis is a fact nowadays due to the advantages they present. Following the trend of providing tools for diagnosis, this research aimed to develop a prototype mobile application for the identification of oral lesions, including potentially malignant lesions, based on convolutional neural networks, as early detection of indications of possible types of cancer in the oral cavity. A mobile application was developed for the Android operating system that implemented the TensorFlow library and the Mobilenet V2 convolutional neural network model. The training of the model was performed by transfer learning with a database of 500 images distributed in five classes for recognition (Leukoplakia, Herpes Simplex Virus Type 1, Aphthous stomatitis, Nicotinic stomatitis, and No lesion). The 80% of the images were used for training and 20% for validation. It was obtained that the application presented at least 80% precision in the recognition of four class. The f1-score and area under curve metrics were used to evaluate performance. The developed mobile application presented an acceptable performance with metrics higher than 75% for the recognition of three lesions, on the other hand, it yielded an unfavorable performance lower than 70% for identifying nicotinic stomatitis cases with the chosen dataset.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

View full text Add to dashboard Cite

show abstract

“…The CNN achieved an overall AUC of [ 91%, which was similar to the average output predications of 21 dermatologists. Many studies since then have leveraged transfer learning to classify lesions into a number of skin cancer classes and determine the probability of malignancy; these studies showed comparable accuracy, AUROC, sensitivity, and/or specificity to board-certified dermatologists or dermatologists in training [19,[31][32][33][34][35][36][37][38][39]. It is also important to note the average dermatologist's diagnostic accuracy (e.g., sensitivity and specificity) when evaluating ML models for general screening.…”

Section: Melanomamentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

185

108

View full text Add to dashboard Cite

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

show abstract

“…Neural networks different models modifications are increasingly common. They contain deep learning algorithms [13], deep convolutional neural networks (VGGNet convolutional neural network architecture and the transfer learning paradigm) [28], synergic deep learning (SDL). They show great effectiveness in the diagnosis of skin lesions.…”

Section: Classifiers Based On Deep Convolutional Neural Network (Dcnn)mentioning

confidence: 99%

An Overview of Classification Methods From Dermoscopy Images in Skin Lesion Diagnostic

Michalska¹,

Boyko

2020

IAPGOS

View full text Add to dashboard Cite

The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.

show abstract

Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques

Cited by 143 publications

References 9 publications

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

An Overview of Classification Methods From Dermoscopy Images in Skin Lesion Diagnostic

Contact Info

Product

Resources

About