Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

Abstract

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

“…Various neural network-based approaches have already been developed for detecting diseases from different types of medical images ( Islam et al, 2022 , Nahiduzzaman, Islam et al, 2021 ). Rajpurkar et al (2017) used deep learning on the ChestX-ray14 dataset and developed a model called CheXNet, which contained 121 layers.…”

Recent advances of Transformers in medical image analysis: A comprehensive review

“…However, this method consumes much time and effort, which may not apply to a large‐scale examine of malaria. Thus, Islam et al 112 proposed a novel method based on multiheaded attention mechanism to diagnose the malaria parasite. This model reached 96.41%, 96.99%, 95.88%, 96.44%, and 99.11% for accuracy, precision, recall, f1‐score, and AUC score on testing data set.…”

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

“…Moreover, Faster R-CNN ( Hung and Carpenter, 2017 ; Ren et al, 2017 ) and SPPnet ( Zhou et al, 2018 ) are optimized neural networks used to speed up and enhance identification time. Recent studies demonstrate the potential of CNNs for malaria parasite detection with promising results, such as VGG-19 model by transfer learning mechanism ( Alnussairi and İbrahim, 2022 ; Jameela et al, 2022 ) or transformer-based models to obtain optimized performance parameters ( Islam et al, 2022 ). The general procedure for malaria parasite detection using deep learning imaging methods is represented in the bottom part of Figure 3 .…”