MedFuseNet: Fusion of Multi-Modal Data for Improved Cervical Cancer Diagnostic Accuracy

Timely identification of cervical cancer is essential for effective therapy and enhanced patient outcomes. Conventional imaging methods, although fundamental, frequently inade-quately represent the Detailed characteristics of cancer pathology because they depend on isolated data sources. MedFuseN et tackles these difficulties by presenting an advanced architecture that integrates multi-modal data to improve diagnostic accuracy and dependability significantly. This advanced model employs high-resolution medical imaging and incorporates patient-specific clinical data, establishing a comprehensive analytical foundation for diagnosis. Our dataset includes 4,049 annotated cervical cell images across a spectrum from normal to malignant, each enriched with detailed clinical parameters. MedFuseNet utilizes a hybrid architecture that integrates CNNs for image data and RNNs for sequential clinical data, facilitating a thorough analysis of various data sources. The methodological integration enables MedFuseN et to surpass conventional single-source models, with an accuracy of 98.5 %, alongside significant enhancements in pre-cision (97.9 %) and recall (98.1 %). These substantial diagnostic improvements highlight the promise of multi-modal data fusion in medical imaging, paving the way for creating more sophisticated, AI -driven diagnostic instruments that may revolutionize early cancer diagnosis and treatment approaches.