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Segmentation and classification of breast cancer using novel deep learning architecture

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Abstract

Breast cancer is one of the most frequent cancers in women, and it has a higher mortality rate than other cancers. As a result, early detection is critical. In computer-assisted disease diagnosis, accurate segmentation of the region of interest is a vital concept. The segmentation techniques have been widely used by doctors and physicians to locate the pathology, identify the abnormality, compute the tissue volume, analyze the anatomical structures, and provide treatment. Cancer diagnostic efficiency is based on two aspects: The precision value associated with the segmentation and calculation of the tumor area and the accuracy of the features extracted from the images to categorize the benign or malignant tumors. A novel deep-learning architecture for tumor segmentation is therefore proposed in this study, and machine learning algorithms are used to categorize benign or malignant tumors. The segmentation results improve the decision-making capability of the physicians to identify whether a tumor is malignant or not and normally, the machine learning techniques need expert annotation and pathology reports to identify this. This challenge is overcome in this work with the help of the GoogLeNet architecture used for segmentation. The segmentation results are then offered to the Support Vector Mchine, Decision Tree, Random Forest, and Naïve Bayes classifier to improve their efficiency. Our work has provided better results in terms of accuracy, Jaccard and dice coefficient, sensitivity, and specificity compared to conventional architectures. The proposed model offers an accuracy score of 99.12% which is relatively higher than the other techniques. A 3.78% accuracy improvement is noticed by the proposed model against the AlexNet classifier and the actual increase is 4.61% on average when compared to the existing techniques.

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Authors and Affiliations

  1. Department of Artificial intelligence and Machine learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    S. Ramesh

  2. Department of ECE, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

    S. Sasikala, S. Gomathi, V. Geetha & V. Anbumani

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  1. S. Ramesh
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  2. S. Sasikala
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Correspondence to S. Ramesh.

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Ramesh, S., Sasikala, S., Gomathi, S. et al. Segmentation and classification of breast cancer using novel deep learning architecture. Neural Comput & Applic 34, 16533–16545 (2022). https://doi.org/10.1007/s00521-022-07230-4

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