An Ensemble Approach for Uterine Pathology Classification in MRI Imaging Using Deep Learning

Early detection and efficient treatment of a variety of gynecological disorders depend critically on the categorization of uterine pathology from Magnetic Resonance imaging (MRI). It dives into deep learning to suggest an ensemble method for improved uterine disease classification that taps into the combined power of many models. It intends to enhance accuracy, reduce false positives, and strengthen the generalization capabilities of the classification system by integrating the strengths of convolutional neural networks (CNN), recurrent neural networks (RNN), and a transfer learning model (ResNet). Combine the predictions from each of these distinct models using a variety of ensemble approaches, such as majority voting, stacking, and boosting. Its effectiveness was assessed utilizing performance assessment measures such as accuracy, precision, recall and F1-Score. The outcomes showed that ensemble strategies, notably stacking, outperformed individual models in several ways. The ensemble technique has great promise for developing the area of medical imaging and might revolutionize the detection of uterine disease by improving accuracy and dependability. To establish the practical application of the technology, more validation studies and clinical evaluations are necessary.