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Published in: Journal of Cancer Research and Clinical Oncology 9/2023

21-01-2023 | Mammography | Research

A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

Authors: Zahra Maghsoodzadeh Sarvestani, Jasem Jamali, Mehdi Taghizadeh, Mohammad Hosein Fatehi Dindarloo

Published in: Journal of Cancer Research and Clinical Oncology | Issue 9/2023

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Abstract

Purpose

Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy.

Methods

The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work.

Results

The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.

Conclusion

The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
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Metadata
Title
A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images
Authors
Zahra Maghsoodzadeh Sarvestani
Jasem Jamali
Mehdi Taghizadeh
Mohammad Hosein Fatehi Dindarloo
Publication date
21-01-2023
Publisher
Springer Berlin Heidelberg
Published in
Journal of Cancer Research and Clinical Oncology / Issue 9/2023
Print ISSN: 0171-5216
Electronic ISSN: 1432-1335
DOI
https://doi.org/10.1007/s00432-023-04571-y

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