Federated Learning for Private AI Diagnosis of Schizophrenia

Federated Learning for Private AI Diagnosis of Schizophrenia

­ Kunal, Santosh Kumar Sahu, Mohammed Azam, Manuj Takkar, Jatin Bansal, Jyoti Prasad Patra
Copyright: © 2024 |Pages: 21
DOI: 10.4018/979-8-3693-1874-4.ch010
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Abstract

This study delves into the realm of federated learning, focusing on its application in the private and accurate artificial intelligence (AI) diagnosis of schizophrenia. Leveraging the collaborative power of distributed datasets without compromising individual privacy, the research investigates the feasibility and effectiveness of federated learning models. The study employs advanced AI algorithms for schizophrenia diagnosis, ensuring the confidentiality of patient data. The results demonstrate the potential of federated learning as a secure and efficient approach for enhancing diagnostic capabilities in mental health, specifically in the context of schizophrenia. This research contributes to the ongoing efforts to harness cutting-edge technologies for improved mental health diagnostics while prioritizing individual privacy.
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1. Introduction

A persistent mental illness called schizophrenia affects about 21 million people globally. Delusions, hallucinations, disordered thinking, and negative symptoms like flat affect and avolition are just a few of the symptoms that define it. Despite being a serious condition that can have a negative impact on a person's life, schizophrenia is treatable. For patients to have better outcomes, schizophrenia must be diagnosed and treated early. However, the technologies used to detect and diagnose schizophrenia today are frequently inefficient and time-consuming.

1.1 Deep Learning

Artificial neural networks are trained in deep learning, a branch of machine learning, to carry out tasks by learning from data. It draws inspiration from how the human brain functions, with its networks of interconnected neurons processing information. Deep learning methods use several layers of computational nodes, commonly referred to as neurons or units, in an effort to represent high-level abstractions in data (Espejo-Garcia et al., 2021). Auto ML and neural network techniques were used to analyze five years' worth of meteorological data to determine if the conditions surrounding rice blast sickness improved or got worse (Espejo-Garcia et al., 2021).

A new technology called deep learning image recognition holds great promise for improving the early recognition and diagnosis of schizophrenia. Artificial neural networks are used in deep learning image recognition, a type of machine learning, to find patterns in photos. These neural networks can learn to recognize patterns that are connected to certain illnesses, such as schizophrenia, after being trained on massive datasets of tagged photos. The field of schizophrenia may benefit from a number of uses of deep learning image recognition, such as:

Early detection: Patterns in facial expressions and other visual cues that are connected to early-stage schizophrenia can be found using deep learning image recognition. This might make it easier to spot those who need early intervention and are at risk for the illness.

Figure 1.

Symptoms of schizophrenia

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