An automatic approach to detect skin cancer utilizing active infrared thermography
Skin cancer is a growing global concern, with cases steadily rising. Typically, malignant moles are identified through visual inspection, using dermatoscopy and patient history. Active thermography has emerged as an effective method to distinguish between malignant and benign lesions. Our previous r...
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402416639X |
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| author | Ricardo F. Soto Sebastián E. Godoy |
| author_facet | Ricardo F. Soto Sebastián E. Godoy |
| author_sort | Ricardo F. Soto |
| collection | DOAJ |
| description | Skin cancer is a growing global concern, with cases steadily rising. Typically, malignant moles are identified through visual inspection, using dermatoscopy and patient history. Active thermography has emerged as an effective method to distinguish between malignant and benign lesions. Our previous research showed that spatio-temporal features can be extracted from suspicious lesions to accurately determine malignancy, which was applied in a distance-based classifier.In this study, we build on that foundation by introducing a set of novel spatial and temporal features that enhance classification accuracy and can be integrated into any machine learning approach. These features were implemented in a support-vector machine classifier to detect malignancy. Notably, our method addresses a common limitation in existing approaches—manual lesion selection—by automating the process using a U-Net convolutional neural network.We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. The detection algorithm relies on accurate lesion segmentation, and its performance was evaluated across four segmentation techniques. At an 85% sensitivity threshold, expert segmentation provided the highest specificity at 87.62%, while non-expert and U-Net segmentations achieved comparable results of 69.63% and 68.80%, respectively. Semi-automatic segmentation lagged behind at 64.45%. This automated detection system performs comparably to high-accuracy methods while offering a more standardized and efficient solution. The proposed automatic system achieves 3% higher accuracy compared to the ResNet152V2 network when processing low-quality images obtained in a clinical setting. |
| format | Article |
| id | doaj-art-587f68aa64f74daa8490921d1ea2c429 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-587f68aa64f74daa8490921d1ea2c4292025-08-20T02:38:11ZengElsevierHeliyon2405-84402024-12-011023e4060810.1016/j.heliyon.2024.e40608An automatic approach to detect skin cancer utilizing active infrared thermographyRicardo F. Soto0Sebastián E. Godoy1Corresponding author.; Department of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción, 4030000, Biobío, ChileDepartment of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción, 4030000, Biobío, ChileSkin cancer is a growing global concern, with cases steadily rising. Typically, malignant moles are identified through visual inspection, using dermatoscopy and patient history. Active thermography has emerged as an effective method to distinguish between malignant and benign lesions. Our previous research showed that spatio-temporal features can be extracted from suspicious lesions to accurately determine malignancy, which was applied in a distance-based classifier.In this study, we build on that foundation by introducing a set of novel spatial and temporal features that enhance classification accuracy and can be integrated into any machine learning approach. These features were implemented in a support-vector machine classifier to detect malignancy. Notably, our method addresses a common limitation in existing approaches—manual lesion selection—by automating the process using a U-Net convolutional neural network.We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. The detection algorithm relies on accurate lesion segmentation, and its performance was evaluated across four segmentation techniques. At an 85% sensitivity threshold, expert segmentation provided the highest specificity at 87.62%, while non-expert and U-Net segmentations achieved comparable results of 69.63% and 68.80%, respectively. Semi-automatic segmentation lagged behind at 64.45%. This automated detection system performs comparably to high-accuracy methods while offering a more standardized and efficient solution. The proposed automatic system achieves 3% higher accuracy compared to the ResNet152V2 network when processing low-quality images obtained in a clinical setting.http://www.sciencedirect.com/science/article/pii/S240584402416639XSkin cancer screeningDynamic thermal imagingImage segmentationConvolutional neural networksMachine learning |
| spellingShingle | Ricardo F. Soto Sebastián E. Godoy An automatic approach to detect skin cancer utilizing active infrared thermography Heliyon Skin cancer screening Dynamic thermal imaging Image segmentation Convolutional neural networks Machine learning |
| title | An automatic approach to detect skin cancer utilizing active infrared thermography |
| title_full | An automatic approach to detect skin cancer utilizing active infrared thermography |
| title_fullStr | An automatic approach to detect skin cancer utilizing active infrared thermography |
| title_full_unstemmed | An automatic approach to detect skin cancer utilizing active infrared thermography |
| title_short | An automatic approach to detect skin cancer utilizing active infrared thermography |
| title_sort | automatic approach to detect skin cancer utilizing active infrared thermography |
| topic | Skin cancer screening Dynamic thermal imaging Image segmentation Convolutional neural networks Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S240584402416639X |
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