A Novel and Adaptive Evaluation Mechanism for Deep Learning Models in Medical Imaging and Disease Recognition

Recent advancements in deep learning (DL) and machine learning (ML) algorithms have led to their extensive use in various fields, including healthcare, finance, image processing, etc. However, selecting the most appropriate evaluation metrics for these algorithms remains a challenge. In this paper, we propose a novel evaluation metrics mechanism that takes into account the problem domain and the specific application area. The proposed mechanism involves randomly assigning weights to each metric for each dimension, applying a correlation operation to measure the degree to which these variables are related, and repeating this process for all stages. The resulting matrix is then used to calculate the final Performance Measurement Matrix (PMM) vector that reflects the most optimal measurement metrics. Our proposed mechanism provides a systematic and objective approach to selecting evaluation metrics for DL and ML algorithms, and can be applied to a wide range of applications. We demonstrate the effectiveness of our mechanism using a case study on medical image processing applications.