Project DetailsThis work focuses on the identification of diverse respiratory diseases through the utilization of advanced machine learning tools and neural networks applied to genomic sequences. The primary objective of our study is to develop a rapid and cost-effective diagnostic tool capable of detecting a range of respiratory illnesses and identifying the different variants of each disease.
The urgency for accurate diagnosis of various respiratory diseases has become paramount, particularly considering the ongoing global COVID-19 pandemic. Additionally, the presence of comorbidities significantly heightens the risk of life-threatening complications. Thus, the development of an efficient diagnostic tool holds immense value in addressing these pressing healthcare challenges.
To achieve our research goals, we researched different types of neural networks and the advantages each one has, in order to create a network that will best work with our specific problem. In addition, we have explored the hyperparameters that affect the quality of the networks and systematically searched for the best set of hyperparameters for each network we examined to achieve optimal results.
