Meme Kanseri Sınıflandırması İçin Yeni Bir Yöntem: Ultrason Görüntülerinde Sinyal İşleme Temelli Bir Yaklaşım

Year 2023, Volume: 10 Issue: 21, 299 - 306, 31.12.2023

Şerife Gengeç Benli Zeynep Ak

https://doi.org/10.54365/adyumbd.1378982

Abstract

Meme kanseri, dünya genelinde kadınlar arasında ölümün önde gelen nedenlerinden biri olup, doğru ve etkili tanı yöntemlerinin önemi vurgulanmaktadır. Bu çalışma, özellikle meme ultrason görüntülerini kullanarak meme kanseri sınıflandırması alanındaki literatüre yeni bir sinyal işleme yaklaşımı kullanan yöntem ile katkı sağlamaktadır. Çalışma, meme ultrason görüntülerinden elde edilen sinyaller ve Varyasyonel Kip Ayrışımı (VMD) alt bantlarından elde edilen sinyalleri kullanan yeni bir yaklaşım sunmaktadır. Elde edilen sonuçlar ile hem orijinal veriden hem de VMD alt bant sinyallerinden elde edilen özelliklerin iyi huylu ve kötü huylu meme ultrason görüntülerini etkili bir şekilde ayırt edilebileceği gösterilmiştir. Kullanılan algoritma ve uygulanan verilere göre elde edilen sınıflandırma performansları değişmektedir. Sayısal sonuçlara göre, dengelenmiş veriler kullanılarak yapay sinir ağları yöntemi ile yapılan çalışma sonucunda en yüksek sınıflandırma performansı elde edilmiş olup, eğri altında kalan alan değeri 0.9971 ve doğruluk değeri 0.9821 olarak elde edilmiştir.

Keywords

Meme ultrason görüntüleri, Varyasyonel kip ayrışımı, Altbandlar, Sınıflandırma.

A Novel Method for Breast Cancer Classification: A Signal Processing-Based Approach in Ultrasound Images

Abstract

Breast cancer, a leading cause of mortality among women worldwide, the importance of accurate and efficient diagnostic methods is emphasized. This study contributes to the literature on breast cancer classification, particularly using breast ultrasound images, with a new method using a signal processing approach. It introduces a novel approach by combining features extracted from signals obtained from breast ultrasound images with signals from Variational Mode Decomposition (VMD) sub-bands. The results demonstrate that utilizing features from both preprocessed raw data and VMD sub-band signals can effectively distinguish benign and malignant breast ultrasound images. Classification performance varied depending on the algorithms and data used. According to the numerical results, the highest classification performance was achieved through the study with balanced data using the artificial neural network method, with an area under the curve value of 0.9971 and an accuracy value of 0.9821.

Keywords

Breast ultrasound images, Variational mode decomposition, Classification., Sub-bands

References

• Fitzmaurice C, Dicker D, et al. The Global Burden of Cancer 2013. JAMA Oncol. 2015;1(4):505–527.
• Lima SM, Kehm RD, Terry MB. Global breast cancer incidence and mortality trends by region, age-groups, and fertility patterns. EClinicalMedicine. 2021;7:38:100985.
• Gong X, Zhou H, Gu Y, Guo Y. Breast ultrasound image classification with hard sample generation and semi-supervised learning. Biomedical Signal Processing and Control. 2023;86:105196.
• Pavithra S, Vanithamani R, Justin J. Computer aided breast cancer detection using ultrasound images. Materials Today. 2020;33(7):4802–4807.
• Mishra A, Roy P, Bandyopadhyay S, Das S. Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach. Expert Systems. 2021;38:e12713.
• Lo CM, Chang RF, Huang CS, Moon WK. Computer-Aided Diagnosis of Breast Tumors Using Textures from Intensity Transformed Sonographic Images. In: 1st Glob. Conf. Biomed. Eng. 9th Asian-Pacific Conf. Med. Biol. Eng. Springer International Publishing, Cham. 2015;124–127.
• Huang Q, Yang F, Liu L, Li X. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Information Sciences. 2015;314:293–310.
• Huang Q, Huang Y, Luo Y, Yuan F, Li X. Segmentation of breast ultrasound image with semantic classification of superpixels. Med Image Anal. 2020;61:101657.
• Liu Y, Ren L, Cao X, Tong Y. Breast tumors recognition based on edge feature extraction using support vector machine. Biomedical Signal Processing and Control. 2020;58:101825.
• Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybernetics and Biomedical Engineering. 2019;39(2):536–560.
• Yi S, Chen Z, Yi L, She F. CAS: Breast Cancer Diagnosis Framework Based on Lesion Region Recognition in Ultrasound Images. Journal of King Saud University - Computer and Information Sciences. 2023;35(8):101707.
• Sadad T, Hussain A, Munir A, Habib M, Ali Khan S, Hussain S, Yang S, Alawairdhi M. Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare. Applied Sciences. 2020;10(6):1900.
• Pacal I. Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2022;12(4):1917–1927.
• Jiménez-Gaona Y, Rodríguez-Álvarez MJ, Lakshminarayanan V. Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences. 2020;10(22):8298.
• Zhang G, Zhao K, Hong Y, Qiu X, Zhang K, Wei B. SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. International Journal of Computer Assisted Radiology Surgery. 2021;16(10):1719–1725.
• Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images, Data in Brief. 2020;28:104863.
• Khusna DA, Nugroho HA, Soesanti I. Performance analysis of edge and detailed preserved speckle noise reduction filters for breast ultrasound images. 2015 2nd International Conference on Information Technology Computer, and Electrical Engineering 2015:76–80.
• Gupta S, Kaur Y. Review of Different Local and Global Contrast Enhancement Techniques for a Digital Image. International Journal of Computer Applications. 2014;100(18):18–23.
• Dragomiretskiy K, Zosso D. Variational Mode Decomposition. IEEE Transactions on Signal Processing. 2014;62(3):531–544.
• He H, Bai Y, Garcia EA, Li S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). 2008:1322–1328.
• Tibshirani R. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B. 1996;58(1):267–288.
• Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273–297.
• Rish I. An Empirical Study of the Naïve Bayes Classifier. IJCAI 2001 Workshop Empiral Methods in Artificial Intelligence. 2001;3(22):41-46.
• Zhang G, Hu MY, Eddy Patuwo B, Indro DC. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Opererational Research. 1999:116(1):16–32.