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DeepakBolleddu/ChestAbnormalityDetection

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App README

🫁 Chest X-ray Abnormality Detection

AI-powered detection and classification of thoracic abnormalities in chest X-rays using YOLOv5.

🎯 Features

—Upload Chest X-rays: Support for PNG, JPG, JPEG formats
—YOLOv5 Detection: Deep learning model trained on VinBigData dataset
—14 Abnormality Classes: Detects critical thoracic findings
—Visual Results: Bounding boxes highlighting detected abnormalities
—Confidence Scores: Probability scores for each detection

🏥 Detectable Abnormalities

ID	Abnormality	ID	Abnormality
0	Aortic enlargement	7	Lung Opacity
1	Atelectasis	8	Nodule/Mass
2	Calcification	9	Other lesion
3	Cardiomegaly	10	Pleural effusion
4	Consolidation	11	Pleural thickening
5	ILD	12	Pneumothorax
6	Infiltration	13	Pulmonary fibrosis

🚀 How to Use

1.Upload: Click or drag a chest X-ray image
2.Analyze: Click "Analyze X-Ray" button
3.Results: View detected abnormalities with confidence scores
4.Download: Save the annotated result image

🛠️ Technical Stack

Component	Technology
Backend	Flask (Python)
ML Model	YOLOv5
Training Data	VinBigData Chest X-ray Dataset (18,000 images)
Deployment	Docker on HuggingFace Spaces

📊 API Endpoints

Endpoint	Method	Description
`/`	GET	Main upload interface
`/predict`	POST	Upload and detect (returns HTML)
`/api/detect`	POST	API endpoint (returns JSON)
`/health`	GET	Health check

👨‍💻 Author

Deepak Bolleddu

—GitHub: @DeepakBolleddu
—Project: ChestAbnormalityDetection

⚠️ Disclaimer: This is a demonstration project for educational purposes only. Not intended for clinical diagnosis. Always consult qualified medical professionals for health concerns.