Invariant scattering transform introduces a new area of research that merges signal processing with deep learning for computer vision. Nowadays, deep learning algorithms can solve various problems in the medical sector. Medical images are used to detect diseases such as brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease, and many others. During the pandemic in 2020, machine learning and deep learning played a critical role in detecting COVID-19, which included mutation analysis, prediction, diagnosis, and decision-making. Medical images like X-rays, MRI (magnetic resonance imaging), and CT scans are used for detecting diseases. Another method in deep learning for medical imaging is scattering transform. It builds useful signal representations for image classification. It is a wavelet technique that is impactful for medical image classification problems. This research article discusses scattering transform as an efficient system for medical image analysis, where it's figured by scattering the signal information implemented in a deep convolutional network. A step-by-step case study is manifested in this research work.