Abstract:
Lung diseases such as Covid-19, Pneumonia and
Tuberculosis remains to be among the leading causes of deaths
globally. These diseases present themselves in a similar manner
bearing common signs and symptoms such as coughing, fever,
fatigue and shortness of breaths. To prevent adverse effects
of these diseases and save more lives, early detection and
diagnosis of the aforementioned diseases is necessary. This
paper proposes a deep transfer learning model: ResMultNet50 to assist radiologists in their work while adopting inverse
class weighting approach to handle class imbalance problem.
The proposed approach relied on fine-tuned ResNet-50 for the
diagnostic task of detecting the three respiratory diseases from
chest x-rays. In the study, a data set comprising of 13,188 chest
x-ray images was used and the proposed approach achieved an
average accuracy of 96.12%. This model outperformed other
deep learning models and transfer learning models used in the
previous studies for solving multi-class related problems.
