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Updated: Jun 21, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Asymmetric lesion detection with geometric patterns and CNN-SVM classification.

M A Rasel¹, Sameem Abdul Kareem¹, Zhenli Kwan²

¹Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.

Computers in Biology and Medicine

|July 14, 2024

Summary

This summary is machine-generated.

This study introduces a novel algorithm for analyzing skin lesion shapes in dermoscopic images, improving melanoma diagnosis accuracy. The developed image processing technique effectively identifies asymmetric lesions, aiding clinicians in early detection.

Keywords:

Dermoscopic image Image processing Melanoma-asymmetric Multiclass SVM Pretrained-CNN

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Area of Science:

Dermatology
Medical Imaging
Computer Vision

Background:

Dermoscopic imaging reveals skin structures crucial for diagnosing diseases.
Asymmetric lesion shape is a key criterion for melanoma diagnosis.
Accurate assessment of lesion asymmetry is vital for early detection.

Purpose of the Study:

To develop an automated image processing algorithm for analyzing skin lesion shape.
To assist non-experts in identifying asymmetric lesions based on clinical criteria.
To improve the accuracy of melanoma diagnosis through shape analysis.

Main Methods:

A supervised learning algorithm was developed to analyze lesion geometry.
Clinical assessments were used to label a dataset for symmetry information.
A pre-trained convolutional neural network (CNN) extracted features for a support vector machine (SVM) classifier.

Main Results:

The geometry-based experiment achieved a 99.00% detection rate for asymmetric lesions.
The CNN-based experiment achieved high scores: 94% Kappa, 95% Macro F1, and 97% weighted F1.
The developed classifier outperformed existing state-of-the-art methods in lesion shape classification.

Conclusions:

The proposed algorithm effectively analyzes lesion shape in dermoscopic images.
Automated analysis of lesion asymmetry can significantly aid in melanoma diagnosis.
The study demonstrates the potential of AI in improving dermatological diagnostics.