  Reading and interpreting chest X-ray images is one of the most radiologist's
routines. However, it still can be challenging, even for the most experienced
ones. Therefore, we proposed a multi-model deep learning-based automated chest
X-ray report generator system designed to assist radiologists in their work.
The basic idea of the proposed system is by utilizing multi
binary-classification models for detecting multi abnormalities, with each model
responsible for detecting one abnormality, in a single image. In this study, we
limited the radiology abnormalities detection to only cardiomegaly, lung
effusion, and consolidation. The system generates a radiology report by
performing the following three steps: image pre-processing, utilizing deep
learning models to detect abnormalities, and producing a report. The aim of the
image pre-processing step is to standardize the input by scaling it to 128x128
pixels and slicing it into three segments, which covers the upper, lower, and
middle parts of the lung. After pre-processing, each corresponding model
classifies the image, resulting in a 0 (zero) for no abnormality detected and a
1 (one) for the presence of an abnormality. The prediction outputs of each
model are then concatenated to form a 'result code'. The 'result code' is used
to construct a report by selecting the appropriate pre-determined sentence for
each detected abnormality in the report generation step. The proposed system is
expected to reduce the workload of radiologists and increase the accuracy of
chest X-ray diagnosis.
