A Computational Intelligence Approach for Cancer Detection Using Artificial Neural Network

Dash, Rasmita; Dash, Rajashree; Rautray, Rasmita

doi:10.1007/978-981-16-9873-6_51

Cited by 1 publication

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“…These issues connect biomedicine and bioinformatics with methodologies from the area of computational intelligence to arrive at a solution. The use of computational intelligence technologies enables the identification of key indicators of the existence of malignant cells in cancer patients, such as the degree of risk (high or low) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning System Technology in Identification of Women’s Breast Cancer

2023

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Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.

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Section: Introductionmentioning

confidence: 99%