Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review

Area of Science:

• Dermatology and Computer Science
• Medical Imaging and Artificial Intelligence

Background:

• Malignant melanoma is a deadly cancer with increasing global incidence.
• Early diagnosis significantly improves survival rates, but conventional methods are costly and expertise-dependent.
• Computerized diagnostic systems offer promising accuracy and efficiency for melanoma detection.

Purpose of the Study:

• To conduct a comprehensive survey of digital dermoscopy techniques for melanoma detection and classification.
• To compare existing surveys focusing on preprocessing, segmentation, feature extraction, and classification methods.
• To identify challenges within each step of digital image processing for melanoma diagnosis.

Main Methods:

• Review of digital dermoscopy image processing steps: preprocessing, segmentation, feature extraction/reduction, and classification.
• Comparative analysis of various techniques within each step, including hair removal, contrast stretching, and lesion segmentation.
• Examination of feature selection methods, validation datasets, classification algorithms, and performance metrics.

Main Results:

• Detailed summary of preprocessing techniques and their challenges (e.g., hair removal, contrast stretching).
• Analysis of lesion segmentation methods and feature extraction techniques, including associated difficulties.
• Identification of limitations in current digital systems contributing to suboptimal performance.

Conclusions:

• Digital dermoscopy involves complex image processing steps, each presenting unique challenges.
• Understanding these challenges is key to improving the performance of automated melanoma detection systems.
• Future research directions are outlined to enhance the accuracy and reliability of computerized melanoma diagnosis.