TITLE:
A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification
AUTHORS:
Nada Al Zahrani, Ramdane Hedjar, Mohamed Mekhtiche, Mohamed Bencherif, Taha Al Fakih, Fattoh Al-Qershi, Muna Alrazghan
KEYWORDS:
Pneumonia, Thoracic Pathology, COVID-19, Deep Learning, Multi-Task Learning
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.4,
April
24,
2024
ABSTRACT: Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.