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

TL;DR

This paper introduces AnoMalNet, a deep autoencoder-based outlier detection method for classifying malaria-infected cells, effectively handling extreme class imbalance without requiring positive samples during training.

Contribution

The study presents a novel outlier detection approach using autoencoders for disease classification in highly imbalanced datasets, outperforming existing models.

Findings

Achieved 98.49% accuracy and 100% recall on malaria cell classification.

Outperforms large deep learning models and previous methods.

Effective in classifying disease with only negative samples during training.

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 fair share of drawbacks. In this research, we propose an outlier detection based binary medical image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively, performing better than large deep learning models and other published works. As our proposed approach can provide competitive results without needing the disease-positive samples during training, it should prove to be useful in binary disease classification on imbalanced datasets.