Lung Cancer Detection in Chest X-Ray Images Empowered by 3D Computed Tomography Deep Convolutional Radiomics (CXRClear)
Objective/Contributions:
Cancer is treatable if it is discovered at an early stage, and lung cancer screening is a critical component in a preventive care protocol. Although CT imaging affords higher spatial resolution and 3D density information than digital chest X-rays, there are still limitations to having it as a cheap and fast method for rural areas outreach. These limitations are outlined in cost, limited access, and portability issues. Chest X-rays modality has been developed recently with the preface of high-resolution digital X-rays however, the smallest observable nodule size is limited to 1-2 cm. To detect such small-size cancer at an early stage, CT is the ideal solution.
The overall objectives achieved in the project are as follows:
- Develop a practical lung cancer detection system that resolves the issue related to detecting small and medium-sized tumors in Chest X-rays.
- Identify the stratification features that define small, medium, and large-size cancer from CT.
- Develop a projection of those features to CXR data so we can perform screening and detection of different-size cancer areas from CXR standalone.
- Unified Dataset has been created.
- Assistive tools such as rib suppression have been developed to assist with X-ray scan reading.
Outcome: Publications
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Kareem Elgohary, Samar Ibrahim, Sahar Selim and Mustafa Elattar (2023). A CAD system for lung cancer detection using Chest X-ray: A Review. In: Fournier-Viger, P., Hassan, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2022. Communications in Computer and Information Science. Springer, Cham.
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Ibrahim, S., Elgohary, K., Higazy, M., Mohannad, T., Selim, S., Elattar, M. (2022). Lung Segmentation Using ResUnet++ Powered by Variational Auto Encoder-Based Enhancement in Chest X-ray Images. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham.
