Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images
Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Ramani Selvanambi
How to Cite?
Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Ramani Selvanambi, "Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 170-178, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P219
One of the most common diseases in the world is cancer. There are many different forms of cancer, and one of the most frequent is breast cancer. Breast cancer can affect anybody, but it most usually affects women. Breast cancer can be cured quickly with early identification and a better knowledge of the illness. A computer-aided diagnostic (CAD) system allows us to uncover several ways to identify and diagnose cancer problems. The primary motivation is for accurate detection to detect cancer as soon as possible. Pre-processing, segmentation, feature extraction, and classification are the four critical steps of detection and identification. Pre-processing techniques employed in this study include median filtering and histogram equalization. For segmentation, a hybrid technique is utilized, and for feature extraction, fundamental methods are applied. For classification, the Support Vector Machine (SVM) is proposed and employed. SVM`s accuracy is then compared to that of other machine learning approaches such as boosted tree (BT), random forest (RF), Naive Bayes (NB), and convolutional neural network (CNN). The results obtained are tabulated, and an accuracy of 93.4% is obtained from the SVM classifier.
Ultrasound images, Histogram equalization, Support vector machine, Convolutional neural network, Accuracy.
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