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Refining skin lesions classification performance using geometric features of superpixels.

Simona Moldovanu1,2, Mihaela Miron1, Cristinel-Gabriel Rusu2,3

  • 1Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008, Galati, Romania.

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This study enhances skin lesion detection using improved Simple Linear Iterative Clustering (iSLIC) superpixels for accurate melanoma and nevus classification. The novel approach improves diagnostic accuracy in dermoscopy images.

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Computational Dermatology

Background:

  • Accurate detection and classification of skin lesions, such as melanoma and nevi, are critical for early diagnosis and treatment.
  • Dermoscopy images provide detailed views of skin lesions, but automated analysis remains challenging.
  • Existing methods often struggle with precise segmentation and classification, leading to potential diagnostic errors.

Purpose of the Study:

  • To introduce an improved Simple Linear Iterative Clustering (iSLIC) superpixels algorithm for enhanced skin lesion segmentation in dermoscopy images.
  • To develop a robust method for discriminating between melanoma and nevi without false negatives.
  • To evaluate the performance of machine learning and neural network classifiers using extracted features from segmented superpixels.

Main Methods:

  • Proposed an improved Simple Linear Iterative Clustering (iSLIC) algorithm for superpixel generation and image segmentation.
  • Utilized a local graph cut method to identify and isolate regions of interest (skin lesions).
  • Extracted shape and geometric features from segmented superpixels and fed them into various machine learning (Random Forest, SVM, AdaBoost, KNN, DT, GNB) and neural network (PRNN, FFNN, 1D-CNN) classifiers.

Main Results:

  • The iSLIC algorithm effectively segmented skin lesions, discarding background superpixels.
  • The proposed method achieved high accuracy in classifying skin lesions, outperforming existing state-of-the-art methods.
  • Evaluations on the 7-Point MED-NODE and PAD-UFES-20 datasets demonstrated the superior performance of the developed approach.

Conclusions:

  • The integration of iSLIC superpixels and advanced machine learning/neural network models offers a powerful tool for accurate skin lesion detection and classification.
  • This method shows significant potential for improving the diagnostic accuracy of melanoma and nevus identification in clinical settings.
  • The proposed approach successfully addresses the challenge of false negatives in skin lesion classification.