IMed-CNN: Ensemble Learning Approach With Systematic Model Dropout for Enhanced Medical Image Classification Using Image Channels and Pixel Intervals

To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image classification tasks. Despite numerous deep learning (DL) models were developed to solve the generalization problem in DL through ensemble learning, most of the models lacks the evaluation of the effects of each ensemble member for the final ensemble model. Additionally, existing ensemble models tend to include huge number of parameters and use images only in RGB channels or greyscale forms missing the crucial representations of the dataset to reach robust classification outcomes, particularly in medical applications. In this study, novel ensemble model IMed-CNN proposed solution to above-mentioned gaps in the field by introducing the data in ten various forms to the model and applying systematic model dropout (SMDE) with unique true prediction (UTP) analysis which insures to choose only useful ensemble members. To verify the performance of the IMed-CNN, extensive experiments was designed for testing. Results illustrate that IMed-CNN outperformed baseline models.