AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder

arXiv:2303.05789v2 Announce Type: replace-cross
Abstract: Class imbalance is a pervasive issue in the field of disease classification from medical images. It is necessary to balance out the class distribution while training a model for decent results. However, in the case of rare medical diseases, images from affected patients are much harder to come by compared to images from non-affected patients, resulting in unwanted class imbalance. Various processes of tackling class imbalance issues have been explored so far, each having its …

abstract arxiv autoencoder balance case class classification cs.cv detection disease diseases distribution eess.iv image images issue malaria medical outlier patients training type