Research Information System
Other, pp.56, 2024
This research aims to explore the potential of using generative AI in the diagnosis and classification of brain cancer, offering a significant innovation by providing more accurate and faster diagnoses compared to current medical methods. Early detection of aggressive brain tumors, such as glioblastoma, can significantly improve patients' lifespan and quality of life. By learning from large datasets, generative AI can better understand the biological and genetic diversity of tumors, thus providing more accurate diagnoses. Data Collection and Preprocessing: Medical imaging data such as
MRI and CT scans will be combined with genomic, proteomic, and metabolomic data to create a comprehensive dataset. Model Training and Development: Deep learning models like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) will be trained for tumor segmentation and classification. GANs will be used to augment training data and provide additional data for rare tumor types. Simulation and Validation: The developed models will be validated using preclinical and clinical data to ensure their applicability in diagnosis and treatment planning.
Accuracy and reliability tests will be conducted throughout this process. The project will be managed by a multidisciplinary team of data scientists, bioinformatics experts, oncologists, and radiologists. Detailed planning and coordination will be ensured for each phase of the project, with data security and confidentiality maintained at the highest level. Adherence to ethical guidelines will be a fundamental principle of the project. The results of this research have the potential for widespread application in the diagnosis and treatment of brain cancer. Generative AI-based approaches will enable faster and more accurate diagnosis of tumors, aiding in the development of personalized treatment plans. This will enhance patient quality of life and improve the efficiency of healthcare services. Additionally, the data obtained and models developed could be applied to the diagnosis and treatment of other cancer types, offering broad innovation potential in the field of healthcare.