Dual deterministic model based on deep neural network for the classification of pneumonia

Article type: Research Article

Authors: Mustafa Khan, Muhammad^{a; *} | UI Islam, Muhammad Saif^b | Siddiqui, Ali Akbar^a | Qadri, Muhammad Tahir^a

Affiliations: [a] Sir Syed University of Engineering and Technology, Karachi, Pakistan | [b] National University of Computer and Emerging Sciences, Lahore, Pakistan

Correspondence: [*] Corresponding author: Muhammad Mustafa Khan, Sir Syed University of Engineering and Technology, Karachi, Pakistan. E-mail: [email protected].

Abstract: Pneumonia is a disease caused by the virus (flu, respiratory Syncytial Virus) or bacteria. It can be fatal if not diagnosed and treated at an early stage. Chest X-rays have been widely utilized to diagnose such abnormalities with high exactitude and are primarily responsible for the augment real-world diagnosis process. Poor availability of authentic data and yardstick-based approaches and studies complicates the comparison process and identifying the safest recognition method. In this paper, a Dual Deterministic Model (DD-M) is proposed based on a Deep Neural network that would identify Pneumonia from chest X-ray and distinguish the cause in case of either viral or bacterial infection at an efficiency equivalent of an active radiologist. To accomplish the automated task of the proposed algorithm, an automatic computer-aided system is necessary. The proposed algorithm incorporates deep learning techniques to understand radiographic imaging better. The results were evaluated after implementing the proposed algorithm where; it reveals various aspects of the chest infected with Pneumonia compared to the healthy individual with approximately 97.45% accuracy and distinguishes between the viral and bacterial infection with the efficiency of 88.41%. The proposed algorithm with an improved image dataset will help the doctors diagnose.

Keywords: Viral Pneumonia, Bacterial Pneumonia, deep learning, dual deterministic model (DD-M), convolutional neural network (CNN)

DOI: 10.3233/IDT-220192

Journal: Intelligent Decision Technologies, vol. 17, no. 3, pp. 641-654, 2023