Automated Skin Lesion Classification on Ultrasound Images

Marosán-Vilimszky Péter; Szalai Klára; Horváth András; Csabai Domonkos; Füzesi Krisztián; Csány Gergely; Gyöngy Miklós: Automated Skin Lesion Classification on Ultrasound Images.
DIAGNOSTICS, 11 (7). ISSN 2075-4418 (2021)

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Folyóiratcikk

Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
Marosán-Vilimszky Péter		10062909
Szalai Klára		10076076
Horváth András	0000-0001-5855-4186	10029872
Csabai Domonkos
Füzesi Krisztián
Csány Gergely		10051304
Gyöngy Miklós		10020416

Absztrakt (kivonat):

The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.

Folyóirat címe:

DIAGNOSTICS

Megjelenés éve:

2021

Kötet:

Szám:

ISSN:

2075-4418

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)