Biosignal Smocking Detection Of X-ray Images

• Author(s):

  Mrs. Lavanya | J. Karthik | G. Sudheer | G. Rohith Yadav

• Keywords:

  Deep Learning, Machine Learning, Lung Image Classification, Viral Pneumonia Detection, Smoking-Induced Lung Damage, Chest X-ray Analysis, CT Scan Imaging, Convolutional Neural Network (CNN), EfficientNetB0, Transfer Learning, Medical Image Processing, Fea

• Abstract:

  Lung Diseases Such As Viral Pneumonia And Smoking-induced Lung Damage Are Major Global Health Concerns Responsible For Millions Of Deaths Each Year. Early And Accurate Detection Of These Conditions Is Essential For Timely Medical Intervention And Treatment. This Project Presents A Deep Learning–based Image Classification Model For Automated Identification Of Viral Pneumonia And Lung Damage Caused By Smoking Using Chest X-ray And CT Images. The Proposed System Leverages Transfer Learning With The EfficientNetB0 Architecture, Which Extracts High-level Visual Features From Lung Images And Classifies Them Into Two Categories. The Dataset Is Preprocessed Through Normalization And Image Augmentation To Enhance Generalization And Reduce Overfitting. The Model Is Trained Using Binary Cross-entropy Loss And Optimized With The Adam Optimizer To Achieve High Accuracy And Robustness. Experimental Results Demonstrate The Model’s Capability To Distinguish Between Viral Pneumonia And Smoker-affected Lungs Effectively, Supporting Radiologists In Diagnostic Decision-making. This System Offers A Reliable, Efficient, And Scalable AI-driven Approach For Medical Imaging Analysis And Contributes To The Advancement Of Computer-aided Diagnosis In Pulmonary Healthcare.

---

Other Details

• Paper id:

  IJSARTV12I5105342

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-13

Download Article