A Comparative Study of Deep Learning Approaches for Amyotrophic Lateral Sclerosis Super-Resolution Image Classification

Abstract

Amyotrophic lateral sclerosis is a fatal degenerative neurological disease known as motor neuron disease. According to research, amyotrophic lateral sclerosis affects nerve cells related to movement in the brain and spinal cord, leading to the death of motor neurons, making the brain unable to control muscle movement, and resulting in a significant loss of motor nerve cells, leading to muscle atrophy. In the early stages of ALS, symptoms are mild, often go unnoticed, and can easily be confused with other diseases. Patients may only feel symptoms such as weakness, convulsions, and fatigue, gradually develop muscle atrophy all over the body, difficulty swallowing, and finally die of respiratory failure. TDP-43 is a DNA/RNA binding protein typically located in the nucleus regulating various steps of RNA metabolism. Meanwhile, TDP-43 is the most prominent pathological protein in the characteristics of ALS patients.[1] TDP-43 is depleted in the nucleus in nearly all ALS cases but accumulates in the cytoplasm.
Deep learning is learning the internal laws and representation levels of sample data. The information obtained from these learning processes can significantly help interpret data such as text, images, and sounds. Its goal is to enable machines to analyze and learn like humans and recognize data. Additionally, super-resolution microscopy can be used to observe the conformation of proteins in biological samples, thereby gaining insight into the structure and assembly mechanism of TDP-43 aggregates.
This project aims to use super-resolution images for machine learning to build a model for classifying ALS patients from non-ALS patients. At the same time, multiple neural network models are used for training and then compared to select the model with the highest accuracy.