Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) are achieved.
AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized Deep CNN architecture
A Multi-Task Deep Learning Framework for Skin Lesion Classification, ABCDE Feature Quantification, and Evolution Simulation
Department of Chemistry, University of Cambridge, Cambridge, United Kingdom（剑桥大学化学系，剑桥，英国）；Harvard Ophthalmology AI Lab, Harvard Medical School, Boston, MA, USA（哈佛医学院眼科人工智能实验室，哈佛医学院，波士顿，马萨诸塞州，美国）
Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
Pakistan Navy Engineering College (PNEC)（巴基斯坦海军工程学院）；National University of Sciences & Technology (NUST)（国家科技大学）；Electronics and Power Engineering (EPE)（电子与动力工程系）
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
German Aerospace Center (DLR)（德国航空航天中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）
Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms
Federal University of Espírito Santo (UFES)（圣埃斯皮里图联邦大学）；Secretary of Health of the Espírito Santo State（圣埃斯皮里图州卫生部）；University Hospital Cassiano Antônio Moraes (HUCAM)（卡西亚诺·安东尼奥·莫赖斯大学医院）
