Leveraging image fusion and transfer learning for enhanced tumor diagnosis

Abstrak

In India, the number of brain tumor cases are increasing rapidly. Compared to the current treatment approaches there is a need for efficient and advanced diagnostic approaches. Doctor primarily use magnetic resonance imaging (MRI) and computed tomography (CT) scans for diagnosis, each with its own set of advantages and limitations. Accurate diagnosis of brain tumors requires detailed information about the tumor’s type, size, location, and proliferation rate. But all this information must be estimated accurately and precisely. Even though MRI and CT provide anatomical information regarding tumors, they may not always correctly classify the tumor hence, often requiring the additional biopsy for more detailed analysis. This paper aims to demonstrate the fusion of MRI and CT scans by generating a fused image using transfer learning with convolution neural networks (CNN) that possesses all the important information from both modalities yields better results than using MRI and CT scans alone. This fused scan has the potential to highlight subtle details that may be overlooked on individual scans. This proposed system retains all the best possible functionalities for medical approaches to brain tumor detection. It paves the way to offer a much more impactful healthcare approach to patients by delivering accurate and reliable statistics, enhancing the diagnostic accuracy (96.43%) precedent to any single modality.