In a recent academic contribution, a new research paper titled "Utilizing Collaborating Biomedical Deep Model for Diagnosis of Ultrasonography Tumor Images" was published by Dr. Maytham Nabeel Meqdad from the Intelligent Medical Systems Department at Al-Mustaqbal University, in collaboration with an international researcher from Noroff University in the Kingdom of Norway. The paper was published by Elsevier in the Al-Mustaqbal Journal of Sustainability in Engineering Sciences (AJSES), which is issued by Al-Mustaqbal University and classified among the Iraqi Academic Scientific Journals.
The research aims to enhance the diagnosis of breast cancer tumors in clinical ultrasound images using artificial intelligence models, focusing on methods that reduce errors and speed up the diagnostic process at a lower cost. The proposed method utilizes a federated learning model, where each client (such as a hospital or clinic) retains its own data and trains the model on this local data. Subsequently, the server model is updated based on the parameters obtained from the trained clients.
The primary innovation in this research is the use of a collaborative medical model where the internal features of the models are shared among each other. This means that the model benefits from the knowledge gained from various datasets without needing to transfer the actual data between institutions, thereby improving diagnostic performance while reducing costs and mitigating data protection risks.
The main objective of the research is to enhance the accuracy of classifying ultrasound images of tumors, enabling rapid and accurate diagnosis. It emphasizes artificial intelligence techniques based on deep learning and collaborative learning as a means to improve performance without sacrificing privacy or complicating data protection.
Title and Link to the Research:
Utilizing Collaborating Biomedical Deep Model for Diagnosis of Ultrasonography Tumor Images
Link to the research : https://ajses.uomus.edu.iq/home/vol2/iss1/1/