The role of AI classifiers in skin cancer images

Background: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods: The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results: The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion: Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.info:eu-repo/semantics/publishedVersio

Comparison of machine learning strategies for infrared thermography of skin cancer

Objective: The aim of this work was to explore the potential of infrared thermal imaging as an aiding tool for the diagnosis of skin cancer lesions, using artificial intelligence methods. Methods: Thermal parameters of skin tumours were retrieved from thermograms and used as input features for two machine learning based strategies: ensemble learning and deep learning. Results: The deep learning strategy outperformed the ensemble learning one, showing good predictive performance for the differentiation of melanoma and nevi (Precision = 0.9665, Recall = 0.9411, f1-score = 0.9536, ROC(AUC) = 0.9185) and melanoma and non-melanoma skin cancer (Precision = 0.9259, Recall = 0.8852, f1score = 0.9051, ROC(AUC) = 0.901). Conclusion: IRT imaging combined with deep learning techniques is promising for simplifying and accelerating the diagnosis of skin cancer. Significance: Despite ongoing awareness campaigns for skin cancer' risk factors, its incidence rate has continuously been growing worldwide, becoming a major public health issue. The standard first detection method - dermoscopy -, is largely experience-dependent and mostly used to assess melanocytic lesions. As infrared thermal imaging is an innocuous imaging technique that maps skin surface temperature, which may be associated to pathological states, e.g., tumorous lesions, it could be a potential aiding tool for all skin cancer conditions. The application of artificial intelligence methods to process the collected temperature data can save time and assist health care professionals with low experience levels in the diagnosis task. To the best of our knowledge, this is the first study where a data set of skin cancer thermograms is expanded and used for skin lesion differentiation with a deep learning approach

Comparison of machine learning strategies for infrared thermography of skin cancer

Objective: The aim of this work was to explore the potential of infrared thermal imaging as an aiding tool for the diagnosis of skin cancer lesions, using artificial intelligence methods. Methods: Thermal parameters of skin tumours were retrieved from thermograms and used as input features for two machine learning based strategies: ensemble learning and deep learning. Results: The deep learning strategy outperformed the ensemble learning one, showing good predictive performance for the differentiation of melanoma and nevi (Precision=0.9665, Recall=0.9411, f1-score=0.9536, ROC(AUC)=0.9185) and melanoma and non-melanoma skin cancer (Precision=0.9259, Recall=0.8852, f1-score=0.9051, ROC(AUC)=0.901). Conclusion: IRT imaging combined with deep learning techniques is promising for simplifying and accelerating the diagnosis of skin cancer. Significance: Despite ongoing awareness campaigns for skin cancer’ risk factors, its incidence rate has continuously been growing worldwide, becoming a major public health issue. The standard first detection method – dermoscopy –, is largely experience-dependent and mostly used to assess melanocytic lesions. As infrared thermal imaging is an innocuous imaging technique that maps skin surface temperature, which may be associated to pathological states, e.g., tumorous lesions, it could be a potential aiding tool for all skin cancer conditions. The application of artificial intelligence methods to process the collected temperature data can save time and assist health care professionals with low experience levels in the diagnosis task. To the best of our knowledge, this is the first study where a data set of skin cancer thermograms is expanded and used for skin lesion differentiation with a deep learning approach.info:eu-repo/semantics/publishedVersio

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given