Computer-aided classification of suspicious pigmented lesions using wide-field images. (October 2020)

• Record Type: Journal Article
• Authors: Birkenfeld, Judith S.; Tucker-Schwartz, Jason M.; Soenksen, Luis R.; Avilés-Izquierdo, José A.; Marti-Fuster, Berta
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Machine learning -- Suspicious pigmented lesions -- Computer-aided classification -- Melanoma -- Wide-field images; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105631
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We have optimized a machine learning classification algorithm to distinguish suspicious from non-suspicious skin lesions. We have introduced a suspiciousness score, which is aligned with common macro-screening practice (naked eye examination). Training of our system included an important source of image variability due to acquisition considerations of the database. This approach might be effective to assess the severity of a suspicious skin lesions and help to improve referral accuracy. This approach allows for low-cost image acquisition instruments like digital cameras and mobile phones. Abstract: Background and objective: Early identification of melanoma is conducted through whole-body visual examinations to detect suspicious pigmented lesions, a situation that fluctuates in accuracy depending on the experience and time of the examiner. Computer-aided diagnosis tools for skin lesions are typically trained using pre-selected single-lesion images, taken under controlled conditions, which limits their use in wide-field scenes. Here, we propose a computer-aided classifier system with such input conditions to aid in the rapid identification of suspicious pigmented lesions at the primary care level. Methods: 133 patients with a multitude of skin lesions were recruited for this study. All lesions were examined by a board-certified dermatologist and classified into "suspicious" and "non-suspicious". A new clinical database was acquired and created by taking Wide-Field images of all major body parts with a consumer-grade camera under natural illumination condition and with a consistent source of image variability. 3–8 images were acquired per patient on different sites of the body, and a total of 1759 pigmented lesions were extracted. A machine learning classifier was optimized and build into a computer aided classification system to binary classify each lesion using a suspiciousness score. Results: In a testing set, our computer-aided classification system achieved a sensitivity of 100% for suspicious pigmented lesions that were later confirmed by dermoscopy examination ("SPL_A") and 83.2% for suspicious pigmented lesions that were not confirmed after examination ("SPL_B"). Sensitivity for non-suspicious lesions was 72.1%, and accuracy was 75.9%. With these results we defined a suspiciousness score that is aligned with common macro-screening (naked eye) practices. Conclusions: This work demonstrates that wide-field photography combined with computer-aided classification systems can distinguish suspicious from non-suspicious pigmented lesions, and might be effective to assess the severity of a suspicious pigmented lesions. We believe this approach could be useful to support skin screenings at a population-level.