Invariant Scattering Transform for Medical Imaging

TL;DR

This paper explores the invariant scattering transform as an effective wavelet-based method that combines signal processing and deep learning for improved medical image classification, demonstrated through a detailed case study.

Contribution

It introduces the scattering transform as a novel approach merging wavelet techniques with deep learning for medical image analysis, enhancing classification performance.

Findings

Effective signal representation for medical images

Improved classification accuracy demonstrated in case study

Potential for better disease detection in medical imaging

Abstract

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the 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 at this research work.