Deep learning is a subset of machine learning, which itself is a branch of Artificial Intelligence (AI). It has revolutionized the field of medical imaging by enabling advanced image processing techniques, including image reconstruction. In the context of brain imaging, image reconstruction refers to restoring high- quality brain scan images from noisy, corrupted, or incomplete data, such as low-resolution.
The purpose of this project is to develop a comprehensive deep learning-based system that performs both image reconstruction and tumor detection for medical MRI scans. Initially, the
system is designed to reconstruct high-quality brain images from noisy, low-resolution, or degraded input scans using Convolutional Autoencoders (CAEs). This reconstruction process restores critical anatomical details, ensuring diagnostic accuracy by enhancing the clarity and resolution of the images. Following reconstruction, the system applies Convolutional Neural Networks (CNNs) to accurately detect the presence of tumors.
The similar page applies for kidney.
Brain Output:
1.Glioma
2.Meningioma
3.No tumor
Kidney Output:
1.Tumor
2.No tumor
