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РецензированоEN2025

Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation

Mazharul Islam Tusher (Department of Computer Science, Monroe College, New Rochelle, New York, USA) · Md Refat Hossain (Master of Business Administration, Westcliff University, USA) · Arjina Akter (Department of Public Health, Central Michigan University, Mount Pleasant, Michigan, USA) · Md Rayhan Hassan Mahin (Department of Computer Science, Monroe University, New Rochelle, USA) · Sharmin Sultana Akhi (Department of Computer Science, Monroe University, USA) · MD Sajedul Karim Chy (Department of Business Administration, Washington University of Science and Technology, USA) · Mahfuz Haider (Clinical Operations Analyst, Department of Clinical Operations, University of Virginia Physicians Group) · Sadia Akter (Department of Business Administration, International American University, USA) · Md Minzamul Hasan (Doctor of Business Administration (DBA), College of Business, Westcliff University, USA) · Mujiba Shaima (Department of Computer Science. Monroe University, NY)

Международный журнал медицинских наук и исследований в области общественного здравоохранения · 63-72 · 2025

Аннотация

Early detection of lung cancer significantly improves patient survival yet remains a challenge due to the subtle nature of early-stage radiological features. This study proposes a multimodal deep learning framework that combines convolutional neural networks (CNN) with dense neural networks (DNN) to enhance early-stage lung cancer prediction. A curated dataset comprising 2,000 patient records with CT scans and clinical metadata was preprocessed and used to train multiple models. The hybrid CNN-DNN model achieved the highest performance with an accuracy of 96.4%, precision of 95.8%, recall of 97.1%, F1-score of 96.4%, and AUC of 0.982, outperforming both traditional machine learning models and standalone CNNs. The integration of imaging and clinical features led to robust and accurate predictions, demonstrating strong potential for real-world clinical application. The results support the deployment of such AI-driven tools in diagnostic workflows to facilitate timely and accurate lung cancer detection.

Доказательный фрагмент

«Early detection of lung cancer significantly improves patient survival yet remains a challenge due to the subtle nature of early-stage radiological features.»

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Early detection of lung cancer significantly improves patient survival yet remains a challenge due to the subtle nature of early-stage radiological features. Полный текст публикации CiteX не хранит — доступ к первоисточнику открывается по DOI согласно лицензии издателя и выбранному плану.

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Журнал
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Ключевые слова
Lung cancer detection, deep learning, convolutional neural networks (CNN), dense neural networks (DNN), early diagnosis, medical imaging, clinical integration, artificial intelligence (AI), hybrid model, computer-aided diagnosis
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