Detection of glaucoma using three-stage training with EfficientNet

[EN] This paper sets forth a methodology that is based on three-stage-training of a state-of-the-art network architecture previously trained on Imagenet, and iteratively finetuned in three steps; freezing first all layers, then re-training a specific number of them and finally training all the architecture from scratch, to achieve a system with high accuracy and reliability. To determine the performance of our technique a dataset consisting of 17.070 color cropped samples of fundus images, and that includes two classes, normal and abnormal, is used. Extensive evaluations using baselines models (VGG16, InceptionV3 and Resnet50) are carried out, in addition to thorough experimentation with the proposed pipeline using variants of EfficientNet and EfficientNetV2. The training procedure is described accurately, putting emphasis on the number of parameters trained, the confusion matrices (with analysis of false positives and false negatives), accuracy, and F1-score obtained at each stage of the proposed methodology. The results achieved show that the intelligent system presented for the task at hand is reliable, presents high precision, its predictions are consistent and the number of parameters needed to train are low compared to other alternatives.This work is supported by the HK Innovation and Technology Commission (InnoHK Project CIMDA), the HK Research Grants Council (Project CityU 11204821) and City University of Hong Kong (Project 9610034). We acknowledge the support of Universitat Politècnica de València; R&D project PID2021-122580NB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and ERDF.De Zarzà, I.; De Curtò, J.; Tavares De Araujo Cesariny Calafate, CM. (2022). Detection of glaucoma using three-stage training with EfficientNet. Intelligent Systems with Applications. 16:1-10. https://doi.org/10.1016/j.iswa.2022.2001401101

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho