Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering

/pdf/a-survey-on-deep-learning-for-skin-lesion-segmentation-1t57bhxg.pdf

A Survey on Deep Learning for Skin Lesion Segmentation

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1753-6405.12972

Early detection of melanoma: a consensus report from the Australian Skin and Skin Cancer Research Centre Melanoma Screening Summit

We present a study on the fusion of pixel data and patient metadata (age, gender, and body location) for improving the classification of skin lesion images. The experiments have been conducted with the ISIC 2019 skin lesion classification challenge data set. Taking two plain convolutional neural networks (CNNs) as a baseline, metadata are merged using either non-neural machine learning methods (tree-based and support vector machines) or shallow neural networks. Results show that shallow neural networks outperform other approaches in all overall evaluation measures. However, despite the increase in the classification accuracy (up to +19.1%), interestingly, the average per-class sensitivity decreases in three out of four cases for CNNs, thus suggesting that using metadata penalizes the prediction accuracy for lower represented classes. A study on the patient metadata shows that age is the most useful metadatum as a decision criterion, followed by body location and gender.

A Study on the Fusion of Pixels and Patient Metadata in CNN-Based Classification of Skin Lesion Images

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging DICOM is a continually evolving standard There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (eg, image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts DICOM can improve AI workflows by encoding derived objects (eg, secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols DICOM can address several important technological and workflow challenges for the implementation of AI However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology

/pdf/the-role-of-dicom-in-artificial-intelligence-for-skin-8g88qncioa.pdf

The Role of DICOM in Artificial Intelligence for Skin Disease.

Artificial Intelligence Approach in Melanoma

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Artificial Intelligence Approach in Melanoma