AI-Powered Deep Learning Models for Early Detection and Prognosis of Cancer in Bangladeshi Clinical Settings
Keywords:
Deep Learning, Artificial Intelligence, Early Cancer Detection, Medical Imaging Analysis, Prognostic Modeling, Bangladesh HealthcareAbstract
This study investigates the design, implementation, and clinical impact of AI-powered deep learning models for early cancer detection and prognosis in Bangladeshi healthcare settings. The primary objectives were to enhance diagnostic accuracy, reduce reporting times, and integrate explainable AI into clinical workflows. Using a mixed dataset of histopathology, mammography, CT, and MRI images, models such as DenseNet121 were trained and validated through 5-fold cross-validation and prospective hospital trials. Performance metrics showed accuracies above 94% across cancer types, with Grad-CAM visualizations matching 88% of expert-marked regions. Clinical integration reduced reporting times by up to 50%, improved early detection rates by 12%, and decreased missed diagnoses by 4–6%. These results suggest that localized, explainable AI models can significantly improve diagnostic efficiency and patient outcomes. Policy implications include the need for investment in AI infrastructure, clinician training, and ethical governance to enable sustainable, nationwide adoption.
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