End-to-End Deep Diagnosis of X-ray Images
This work addresses the need for efficient and accurate medical image analysis for healthcare professionals, though it is incremental as it builds on existing architectures like DenseNet-121.
The paper tackles the problem of automated diagnosis from X-ray images by developing an end-to-end deep learning framework that sequentially identifies X-rays, classifies their type, and detects abnormalities, achieving an end-to-end accuracy of 0.91 with task-specific accuracies up to 0.987.
In this work, we present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.