International Journal on Science and Technology

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Integrating Spatial And Sequential Analysis For Lung Cancer Detection

Author(s) Lakshmi Priyanka Somineni, Rambhupal M, Dhana Lakshmi Tatiparthi, Chandu sirikonda, Anila Teja Pralayakaveri, Farhana Sultana Shaik
Country India
Abstract Lung cancer is still one of the most common causes of death globally, and thus early diagnosis is critical to enhance survival. The conventional diagnostic procedures usually identify lung cancer at a late stage, making early detection important. This paper presents a deep learning-based model that combines MobileNet for efficient feature extraction and LSTM for analyzing sequential patterns to enhance early lung cancer detection. MobileNet effectively captures key medical image features, while LSTM identifies temporal dependencies, improving diagnostic accuracy. The model’s lightweight architecture allows for real-time deployment, making it particularly useful for mobile-based diagnostics and low-resource settings. With its high accuracy, this AI-powered solution holds great potential for clinical decision-making, early intervention, and cost-effective diagnosis.
Keywords Long Short-Term Memory (LSTM), MobileNet, deep learning, feature extraction.
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-29
Cite This Integrating Spatial And Sequential Analysis For Lung Cancer Detection - Lakshmi Priyanka Somineni, Rambhupal M, Dhana Lakshmi Tatiparthi, Chandu sirikonda, Anila Teja Pralayakaveri, Farhana Sultana Shaik - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2355
DOI https://doi.org/10.71097/IJSAT.v16.i1.2355
Short DOI https://doi.org/g899hc
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