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Pneumonia Is A Prevalent And Life-threatening Respiratory Disease That Affects Individuals Of All Age Groups Worldwide. Timely And Accurate Diagnosis Is Crucial For Effective Patient Management And Treatment. In Recent Years, Deep Learning Techniques Have Shown Great Promise In Automating The Detection Of Pneumonia From Medical Images, Particularly X-ray Radiographs. This Study Focuses On The Development Of A Deep Learning-based System For Pneumonia Detection Using X-ray Images. The Primary Objective Of This Research Is To Design, Implement, And Evaluate A Robust And Accurate Deep Learning Model That Can Aid Medical Professionals In The Rapid And Reliable Identification Of Pneumonia. The Proposed Model Leverages Convolutional Neural Networks (CNNs) And State-of-the-art Deep Learning Architectures To Extract Relevant Features From X-ray Images. These Features Are Then Used For Binary Classification, Distinguishing Between Normal And Pneumonia-affected Cases. In Conclusion, This Study Presents A Comprehensive Exploration Of The Application Of Deep Learning In Pneumonia Detection Using X-ray Images, With The Aim Of Advancing The State Of The Art In Automated Diagnostic Tools For Pneumonia In The Medical Field.

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