UNVEILING THE BRCA GENE: USING AI-DRIVEN MEDICAL IMAGING INFORMATICS TO REVOLUTIONISE CANCER DIAGNOSTICS

Abstract

The two most well-known genes inculpated in transcriptional control and DNA repair are breast cancer 1 and breast cancer 2, which are connected to breast tumor, ovarian tumor, and also to lung tumor. In order to elucidate mechanisms by which BRCA genes ultimately lead to the development of breast, ovarian, or lung tumors, the current study set out to clarify the expression patterns, mutational patterns, and interaction pathways of BRCA1 and BRCA2. So here in this thesis bioinformatics tools have been used to analyse these two genes. Artificial intelligence application in image informatics is used. It may enhance therapeutic results and increase the value of medical image analysis in yet-to-be-determined ways. Different techniques of imaging like anatomical (x-ray, MRI, ultrasound) and system generated imaging techniques (microscopy, PACs, SPECT) has been discussed in this paper. FIREHOSE analysis was done to check messanger RNA levels. Breast cancer and Breast cancer 2 mutations were checked using cBioPortal study. A KM plotter study was completed to ascertain the predictive role of these genes in the chosen cancer type. After this box plot and stage plot analysis done with GEPIA2 database to check expression in four stages and for a comparison between normal tissue and tumoric tissue. STRING analysis done to show functional interaction between proteins. Additionally,. Deep learning and machine learning approaches for imaging is discussed at later stage. Deep learning techniques in particular are receiving a lot of attention due to their outstanding efficiency in image-recognition tasks in artificial intelligence (AI). They can more effectively and accurately diagnose patients by performing an automated quantitative assessment of complicated medical image properties. Deep learning, particularly image categorization, is being used more and more in the realm of medical images. For the collection and storage of image data, the Digital Imaging and Communication in medicine is frequently utilized. How OMERO and DICOM database is used for digital imaging is discussed further.