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An efficient breast cancer classification and segmentation system by an intelligent gated recurrent framework

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Abstract

One of the most cautious diseases that produced an increased death rate around the world is breast cancer. The early detection of this disease can save the lives of people. Therefore, an efficient detection and segmentation model is required to detect and classify cancer cells. Several past studies required more robust features and have gained more complexity because of the irrelevant features. Hence, a novel Buffalo-based Gated recurrent Cancer cell segmentation (BGRCS) has been implemented for segmenting the cancer cell in the oriented breast MRI images. Initially, the noise features were traced and eliminated using the preprocessing function. Moreover, the segmentation and classification function has been executed with dual classes: cancer and non-cancerous images. Consequently, the disease feature has been tracked for the classified cancerous images, and the buffalo function of the system segmented the traced features. It has earned meaningful features and reduced the computational time to train the system. Finally, the performance was valued and compared with other past studies. The designed framework has gained the highest segmentation accuracy over the compared models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, 522019, India

    Srikanth Busa

  2. Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, 521230, India

    Jayaprada Somala

  3. Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, 522508, India

    K. Kranthi Kumar

  4. Department of Computer Science and Engineering, VIT - AP University, Amaravati, Andhra Pradesh, 522237, India

    Khasim Syed

  5. Department of Computer Science and Engineering, TKR College of Engineering and Technology, Meerpet, Telangana, 500097, India

    K. S. R. Radhika

  6. Department of Computer Science and Engineering, SRK Institute of Technology, Vijayawada, Andhra Pradesh, 521108, India

    Radhika Ankala

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Busa, S., Somala, J., Kumar, K.K. et al. An efficient breast cancer classification and segmentation system by an intelligent gated recurrent framework. Multimed Tools Appl 83, 31567–31586 (2024). https://doi.org/10.1007/s11042-023-16826-4

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