Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%

Effectiveness of interventions to support the early detection of skin cancer through skin self-examination: a systematic review and meta-analysis.

BACKGROUND: As skin cancer incidence rises, there is a need to evaluate early detection interventions by the public using skin self-examination (SSE); however, the literature focuses on primary prevention. No systematic reviews have evaluated the effectiveness of such SSE interventions. OBJECTIVES: To systematically examine, map, appraise and synthesize, qualitatively and quantitatively, studies evaluating the early detection of skin cancer, using SSE interventions. METHODS: This is a systematic review (narrative synthesis and meta-analysis) examining randomized controlled trials (RCTs) and quasiexperimental, observational and qualitative studies, published in English, using PRISMA and National Institute for Health and Care Excellence guidance. The MEDLINE, Embase and PsycINFO databases were searched through to April 2015 (updated in April 2018 using MEDLINE). Risk-of-bias assessment was conducted. RESULTS: Included studies (n = 18), totalling 6836 participants, were derived from 22 papers; these included 12 RCTs and five quasiexperiments and one complex-intervention development. More studies (n = 10) focused on targeting high-risk groups (surveillance) than those at no higher risk (screening) (n = 8). Ten (45%) study interventions were theoretically underpinned. All of the study outcomes were self-reported, behaviour related and nonclinical in nature. Meta-analysis demonstrated the impact of the intervention on the degree of SSE activity from five studies, especially in the short term (up to 4 months) (odds ratio 2·31, 95% confidence interval 1·90-2·82), but with small effect sizes. Risk-of-bias assessment indicated that 61% of the studies (n = 11) were of weak quality. CONCLUSIONS: Four RCTs and a quasiexperimental study indicate that some interventions can enhance SSE activity and so are more likely to aid early detection of skin cancer. However, the actual clinical impact remains unclear, and this is based on overall weak study (evidence) quality

Intraclass clustering-based CNN approach for detection of malignant melanoma

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%

Cats or CAT scans: transfer learning from natural or medical image source datasets?

Transfer learning is a widely used strategy in medical image analysis. Instead of only training a network with a limited amount of data from the target task of interest, we can first train the network with other, potentially larger source datasets, creating a more robust model. The source datasets do not have to be related to the target task. For a classification task in lung CT images, we could use both head CT images, or images of cats, as the source. While head CT images appear more similar to lung CT images, the number and diversity of cat images might lead to a better model overall. In this survey we review a number of papers that have performed similar comparisons. Although the answer to which strategy is best seems to be "it depends", we discuss a number of research directions we need to take as a community, to gain more understanding of this topic.Comment: Accepted to Current Opinion in Biomedical Engineerin

A novel approach to segment skin lesions in dermoscopic images based on a deformable model

Abstract:Dermoscopy is an imaging technique that has been widely used in the diagnosis of skin lesions. However, its accuracy largely depends on the dermatologist's experience; thus, computer-aided diagnosis techniques are required. In this paper, a novel approach based on a deformable model is proposed to handle the segmentation of skin lesions in dermoscopic images. The RGB color space is converted so that the color information contained in the images can be used effectively to differentiate normal skin and skin lesions; and the differences in the color channels are combined together to define the speed function and the stopping criterion of the deformable model. This novel approach is robust against the noise, and provides an effective and flexible segmentation. Two image databases were used to test the performance of the novel approach and the segmentation results obtained were satisfactory. Quantitative analysis on 250 dermoscopic images showed that the novel algorithm outperformed other state-of-the-art algorithms. Also, using comparative data, the reliability and the implementation issues of the approach are discussed in this paper

Main findings and advances in bioinformatics and biomedical engineeringIWBBIO 2018

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00.In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR)