Open Access

ARTICLE

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

K. Nagalakshmi^1,*, S. Dr. Suriya²

1 Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, 626115, Tamil Nadu, India
2 Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, 641004, Tamil Nadu, India

* Corresponding Author: K. Nagalakshmi. Email:

Computer Systems Science and Engineering 2023, 44(1), 501-517. https://doi.org/10.32604/csse.2023.022687

Received 15 August 2021; Accepted 28 December 2021; Issue published 01 June 2022

Abstract

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

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Keywords

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