The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study

Abstract

ObjectivesTo investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.MethodsIn total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis.ResultsThe estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners.ConclusionsThis study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions.

The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers

“…Next, to analyze the internal properties for further analysis, the lesion within the objective tumor area should be separated from the surrounding skin in a procedure called segmentation. As it is not practical to manually define areas for all images, many automatic lesion segmentation systems have been reported (17–19), but this step is still a challenging task for engineers (16).…”

A Review of Prevalent Methods for Automatic Skin Lesion Diagnosis

“…In recent times, the literature has seen a great improvement in automating lesion image segmentation from the surrounding healthy skin parts for the purpose of achieving automated diagnosis of such lesion images. However, Chang et al [239] argued that it is impractical to perform fully automatic segmentation on all skin lesion images due to reasons such as complexities surrounding acquisition of lesion images.…”

Differential diagnosis of leukoplakia versus lichen planus of the oral mucosa based on digital texture analysis in intraoral photography

“…Such attempts have been made in dermatology and oral surgery, leading to the conclusion that manual segmentation by general practitioners is feasible in the described computer-aided diagnostic system for classifying benign and malignant skin lesions. 5,6 To date, no studies have been published on that topic for oral mucosa diagnosis and automated image segmentation.…”