Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption

Abstract

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net, and ResNet can be considered as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant, usually named U-Net Segmentation, has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw in high memory and CPU consumption while moving the whole feature map to the corresponding decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problems with the efficient use of memory consumption. The proposed model has been evaluated on a standard benchmark dataset of Eye Net, having 32 classes of retinal diseases. From experimental evaluation, it has been concluded that the proposed model performs better regarding memory management and accuracy. The overall comparison has been performed based on precision, recall, and accuracy with different numbers of epochs and time consumption by each step. The proposed technique achieved an accuracy of 95% on the Eye-net dataset.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

“…Also, to efficiently handle large data and deliver timely predictions, a network connection with high bandwidth and low latency is mandatory. An alternative approach is to use each client's data to train the ML model and then distribute copies of the trained model to each participant [20][21][22][23]. This way, data doesn't need to be moved when new insights are gained, as each owner has their model.…”