An innovative approach for investigation of virus borne diseases using deep learning techniques

Abstract

The outbreak of virus-borne diseases is a global health challenge that requires accurate and timely diagnosis and treatment. Traditional methods of diagnosis and treatment often rely on manual examination and interpretation of medical images and patient data, leading to inaccuraciesand delays in diagnosis. This thesis proposes an innovative approach for the investigation of virus-borne diseases using deep learning techniques.The approach leverages the power of deep learning algorithms, including convolution neural networks (CNNs) and long short-term memory (LSTM) networks, to analyze large datasets of medical images and patient data accurately. newlineThe study focuses on the diagnosis of virus-borne diseases such as COVID-19, Dengue fever, and Zika virus. The approach consists of threemain stages: data preparation, model development, and model evaluation.In the data preparation stage, a dataset of medical images and patient datais collected and preprocessed. The dataset includes images of chest X-rays and CT scans of patients with COVID-19 and healthy individuals, as well as patient data such as age, gender, and medical history. In the model development stage, deep learning models are developed using CNNs and LSTMs to analyze the medical images and patient data. The CNNs are used to extract features from the medical images, while the LSTMs are used to analyze the patient data. The models are trained using a combination of supervised and unsupervised learning techniques, including transfer learning and data augmentation. newlineIn the model evaluation stage, the accuracy and reliability of the deep learning models are evaluated using a test dataset of medical images and patient data. The results of the experiments demonstrate that the deep learning models achieved high accuracy in the identification of virus-borne diseases such as COVID-19, Dengue fever, and Zika virus. The proposed approach is highly promising as it can identify viral infections rapidly and accurately, which is critical for timely and effective treatment. newline