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Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.
Vijayarajan, S M; Manoj Kumar, D; Sudha, G; Reddy, A Basi.
Afiliação
  • Vijayarajan SM; Department of Electronics and Communication Engineering, NPR College of Engineering & Technology, Dindigul, Tamil Nadu, India.
  • Manoj Kumar D; Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
  • Sudha G; Department of Biomedical Engineering, Muthayammal Engineering College, Tamil Nadu, India.
  • Reddy AB; Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India.
Microsc Res Tech ; 87(8): 1742-1752, 2024 Aug.
Article em En | MEDLINE | ID: mdl-38501825
ABSTRACT
This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Neoplasias da Mama / Redes Neurais de Computação / Raios Infravermelhos Limite: Female / Humans Idioma: En Revista: Microsc Res Tech Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Neoplasias da Mama / Redes Neurais de Computação / Raios Infravermelhos Limite: Female / Humans Idioma: En Revista: Microsc Res Tech Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia