ABSTRACT
In an imbalanced dataset, a machine learning classifier using traditional imbalance handling methods may achieve good accuracy, but in highly imbalanced datasets, it may over-predict the majority class and ignore the minority class. In the medical domain, failing to correctly estimate the minority class might lead to a false negative, which is concerning in cases of life-threatening illnesses and infectious diseases like Covid-19. Currently, classification in deep learning has a single layered architecture where a neural network is employed. This paper proposes a multilayer design entitled LayNet to address this issue. LayNet aims to lessen the class imbalance by dividing the classes among layers and achieving a balanced class distribution at each layer. To ensure that all the classes are being classified, minor classes are combined to form a single new 'hybrid' class at higher layers. The final layer has no hybrid class and only singleton(distinct) classes. Each layer of the architecture includes a separate model that determines if an input belongs to one class or a hybrid class. If it fits into the hybrid class, it advances to the following layer, which is further categorized within the hybrid class. The method to divide the classes into various architectural levels is also introduced in this paper. The Ocular Disease Intelligent Recognition Dataset, Covid-19 Radiography Dataset, and Retinal OCT Dataset are used to evaluate this methodology. The LayNet architecture performs better on these datasets when the results of the traditional single-layer architecture and the proposed multilayered architecture are compared.
