Design of an Efficient Multimodal Correlation Engine for Smart IoMT Imaging System for Pre-Emptive Analysis of Breast Cancer Stages

Early breast cancer detection and diagnosis remain difficult tasks, especially in areas with little resources in the contemporary era of precision medicine.Existing imaging techniques like mammography, ultrasound, and thermal imaging provide valuable information, but each one's potential is sometimes constrained by exorbitant costs, radiation exposure, and poor accuracy rates.In order to combine data from mammography, ultrasound, optical imaging, and thermal imaging scans, this research suggests a cutting-edge multimodal correlation engine for the Internet of Medical Things (IoMT).The resulting technology makes preemptive breast cancer analysis effective, affordable, and extremely accurate for different scenarios.The study uses the VGGNet 19 architecture for mammography data, but swaps out the fully connected layer with a group of classifiers that includes Naive Bayes, k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Logistic Regression (LR).This strategy guarantees a strong and varied learning approach.Radial Basis Function Networks (RBFNs), which offer a flexible and non-linear classification method, are used to classify ultrasound scans into cancer probabilities after being translated into multidimensional data using Frequency and iVector Analysis.The classified cancer levels are processed on the IoMT cloud, which assists in incremental improvements in the model's performance for real-time scenarios.This performance was tested on the Breast Ultrasound Image (BUSI) dataset, Breast Thermal Image (THERMO) dataset, and Digital Database for Screening Mammography (DDSM) Dataset Samples, where this multifaceted approach has shown a significant increase in precision (12.5%), accuracy (14.9%),Area Under the Curve (AUC, 8.5%), sensitivity (9.4%), and specificity (10.5%) when compared to recent methods.By combining many imaging modalities into a single, effective, and potent diagnostic tool, the suggested approach opens up new possibilities in the early identification of breast cancer types.This strategy offers potential implications for breast cancer pre-emption, especially in environments where resources and access to care are limited.