Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features
By Catarina Barata, Margarida Ruela, Mariana Francisco, Teresa Mendonça, Jorge S. Marques. IEEE Systems Journal, 2014
452 citations recorded by the source corpus.
Subfields: Cell Biology, Industrial and Manufacturing Engineering, Oncology
View Paper: https://www.semanticscholar.org/paper/a674e125dd19b8cda3382b38e251f55b3f40c39e
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In Industrial and Manufacturing Engineering
In Oncology
Citations in the corpus
- Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC) (2016, background): “As a result, community interest has grown, as many centers have begun their own research efforts on automated analysis [24]– [34].”
- Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (2016, methodology): “While an earlier effort to create a public archive of images was made [14, 15], the dataset was too small (200 images) to fully represent scope of the task.”
- A Survey on ensemble learning under the era of deep learning (2021, methodology): “…Description TFE22 Mallat wavelet decomposition (Mallat 1989) TFE23 Empirical mode decomposition (EMD) (Huang et al. 1998) TFE24 Color histogram (Barata et al. 2014) TFE25 Edge histogram (Barata et al. 2014) TFE26 Multi-scale variant of color local binary patters (MSLBP) (Zhu et al. 2010) TFE27…”
- Attention Residual Learning for Skin Lesion Classification (2019, methodology): “[45] proposed a global and local method to classify skin lesions by extracting a set of color and texture features and using them to train a classifier.”
- Deep learning ensembles for melanoma recognition in dermoscopy images (2016, methodology): “A team from the Pedro Hispano Hospital of Portugal sought to evaluate the performance of several (e.g., SVM and kNN) machine learning classifiers based on color, edge, and texture descriptors [22,23].”
- Channel prior convolutional attention for medical image segmentation (2023, methodology): “Following the approach in Guo et al. (2022a); Lee et al. (2020); Wang et al. (2021a), the PH2 dataset (Barata et al., 2014) is used as the test set, and the evaluation metrics employed are DSC and Intersection over Union (IoU).”
- Skin lesion classification from dermoscopic images using deep learning techniques (2017, methodology): “[11] utilizes two different methods for the detection of melanoma in dermoscopy images based on global and local features.”