Breast cancer is a serious disease that inflicts millions of people each year, and the number of cases is increasing. Early detection is the best way to reduce the impact of the disease. Researchers have developed many techniques to detect breast cancer, including the use of histopathology images in CAD systems. This research proposes a technique that combine already fully trained model using adaptive average ensemble, this is different from the literature which uses average ensemble before training and the average ensemble is trained simultaneously. Our approach is different because it used adaptive average ensemble after training which has increased the performance of evaluation metrics. It averages the outputs of every trained model, and every model will have weight according to its accuracy. The accuracy in the adaptive weighted ensemble model has achieved 98% where the accuracy has increased by 1 percent which is better than the best participating model in the ensemble which was 97%. Also, it decreased the numbers of false positive and false negative and enhanced the performance metrics.

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays.

Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution when making predictions. The weighted average or weighted sum ensemble is an extension over voting ensembles that assume all models are equally skillful and make the same proportional contribution to predictions made by the ensemble. Each model is assigned a fixed weight that is multiplied by the prediction made by the model and used in the sum or average prediction calculation. The challenge of this type of ensemble is how to calculate, assign, or search for model weights that result in performance that is better than any contributing model and an ensemble that uses equal model weights. In this tutorial, you will discover how to develop Weighted Average Ensembles for classification and regression. How to Develop a Weighted Average Ensemble With Python Photo by Alaina McDavid, some rights reserved.

A modeling averaging ensemble combines the prediction from each model equally and often results in better performance on average than a given single model. Sometimes there are very good models that we wish to contribute more to an ensemble prediction, and perhaps less skillful models that may be useful but should contribute less to an ensemble prediction. A weighted average ensemble is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance. In this tutorial, you will discover how to develop a weighted average ensemble of deep learning neural network models in Python with Keras. How to Develop a Weighted Average Ensemble for Deep Learning Neural Networks Photo by Simon Matzinger, some rights reserved. Model averaging is an approach to ensemble learning where each ensemble member contributes an equal amount to the final prediction. In the case of regression, the ensemble prediction is calculated as the average of the member predictions.