Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study

TL;DR

This paper explores the use of deep learning, specifically CNNs, to interpret immunofluorescence slides for anti-nuclear antibodies, aiming to improve early detection of immune diseases.

Contribution

It introduces a deep learning-based method for analyzing immunofluorescence slides, demonstrating its effectiveness in a clinical setting for immune disease diagnosis.

Findings

Deep learning improves detection accuracy of immune disease markers.

The CNN-based approach was validated by a clinical immunology department.

The method offers a promising tool for early diagnosis of autoimmune diseases.

Abstract

Nowadays, diseases are increasing in numbers and severity by the hour. Immunity diseases, affecting 8\% of the world population in 2017 according to the World Health Organization (WHO), is a field in medicine worth attention due to the high rate of disease occurrence classified under this category. This work presents an up-to-date review of state-of-the-art immune diseases healthcare solutions. We focus on tackling the issue with modern solutions such as Deep Learning to detect anomalies in the early stages hence providing health practitioners with efficient tools. We rely on advanced deep learning techniques such as Convolutional Neural Networks (CNN) to fulfill our objective of providing an efficient tool while providing a proficient analysis of this solution. The proposed solution was tested and evaluated by the immunology department in the Principal Military Hospital of Instruction of Tunis, which considered it a very helpful tool.