COVID-19 CT and X-ray Detection.
Cairo, Egypt
Source: COVID CT/X-ray report
A supervised machine-learning study for COVID-19 detection from chest CT and X-ray scans using feature extraction, classical classifiers, and ensemble voting.
The project explored whether chest CT and X-ray images could support fast, low-cost COVID-19 screening when PCR access was constrained.
The challenge was to get strong diagnostic classification performance without relying on heavyweight GPU-driven deep learning pipelines.
I structured two supervised classification workflows: one for CT scans and one for chest X-rays, each trained and tested on public medical-imaging datasets.
The models used image feature extraction across raw pixel intensities, HSV color histograms, Haralick texture features, and threshold adjacency statistics, then compared k-NN, SVM variants, Random Forest, MLP, and hard-voting ensembles.
- →Built separate CT and X-ray classifiers for COVID-19 versus non-COVID detection
- →Achieved 98.2% CT accuracy and 99.6% X-ray accuracy with hard-voting ensembles
- →Compared classical feature-extraction pipelines against multiple supervised classifiers
- →Included a lightweight web-app testing flow for novel CT and X-ray inputs
- →Showed that engineered image features plus classical ML could produce strong COVID-19 screening results with modest compute needs.
- →Validated the approach across two imaging modalities instead of a single scan type.
- →Positioned the work as a low-cost screening aid for settings where broad PCR testing was difficult.