A Clinically Inspired Approach for Melanoma classification

Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evol...

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Bibliographic Details
Published inarXiv.org
Main Authors Akundi, Prathyusha, Soumyasis Gun, Jayanthi Sivaswamy
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.06.2021
Subjects
Data analysis
Diagnosis
Fatalities
Melanoma
Outliers (statistics)
Pattern analysis
Pattern recognition
Skin cancer
Systems design
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Summary:Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evolving) criterion. In practice however, a differential approach wherein outliers (ugly duckling) are detected and used to evaluate nevi/lesions. Incorporation of differential recognition in Computer Aided Diagnosis (CAD) systems has not been explored but can be beneficial as it can provide a clinical justification for the derived decision. We present a method for identifying and quantifying ugly ducklings by performing Intra-Patient Comparative Analysis (IPCA) of neighboring nevi. This is then incorporated in a CAD system design for melanoma detection. This design ensures flexibility to handle cases where IPCA is not possible. Our experiments on a public dataset show that the outlier information helps boost the sensitivity of detection by at least 4.1 % and specificity by 4.0 % to 8.9 %, depending on the use of a strong (EfficientNet) or moderately strong (VGG or ResNet) classifier.
ISSN:2331-8422