Genome Sequence Identification Using Neural Network for Breast Cancer Diagnosis

The disease that strikes women the most commonly worldwide is breast cancer. According to the International Agency for Research on Cancer, 685,000 women will pass away from breast cancer worldwide in 2020 after receiving a diagnosis of 2,261,419 cases. With early discovery and diagnosis, the disease's mortality rates can be lowered and patients' lives can be extended. Even though many screening methods have been employed for the early detection of breast cancer, including ultrasound, X-ray mammography, breast magnetic resonance imaging, computed tomography, partial emission tomography, biopsy, and breast temperature measurement, even these are, to some extent, insufficient in screening. The effectiveness and accuracy of these systems can be improved by utilizing a range of AI and deep learning models. The accuracy of a variety of distinct neural network types classified as machine learning and artificial intelligence will be explored, reviewed, and analyzed in this study. Identification, interpretation, and categorization of the genomic sequences relevant to the diagnosis of breast cancer are made much easier by these neural networks.