A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga; Misra, Sanjay; Damaševičius, Robertas

doi:10.1080/08839514.2022.2033473

Cited by 37 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We show that increasing the training set and making the distribution of the RBC categories balanced via data augmentation partially solves these problems, irrespective of the type of microscopy images. This step leads to an overall accuracy of the NN-based prediction ranging between 86-93 % for the three types of imaging techniques tested here that is comparable to performance values reported recently for NN-based malaria detection [29][30][31][32] .…”

Section: Discussionsupporting

confidence: 85%

“…As a common solution for this problem, data augmentation can be used to equalise the number of images in the different categories (ring-, trophozoite-, schizont-stage, and healthy RBCs) by generating more data for the training set 29,30 . This has been demonstrated to improve the performance of the NN 31,32 and to further increase the generalisation ability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing data dimension boosts neural network-based stage-specific malaria detection

Preißinger

Kellermayer

Vértessy

et al. 2022

Preprint

View full text Add to dashboard Cite

Although malaria has been known for more than 4 thousand years, it still imposes a global burden with approx. 240 million annual cases. Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories---healthy ones and three classes of infected ones according to the parasite age---with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension can be straightforwardly generalised for the classification of different parasites, cells and other types of objects.

show abstract

Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Reducing data dimension boosts neural network-based stage-specific malaria detection

Preißinger

Kellermayer

Vértessy

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, we are currently working on trying to improve the efficiency of our approach. In fact, we are assessing to take into account other augmentation procedures, as introduced in [ 81 ]. Moreover, we plan to use other deep learning models such as Swin or Vision transformer, which achieved the best results and have been more recently used in different computer vision tasks.…”

Section: Discussionmentioning

confidence: 99%

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Mabrouk

Dahou

Elaziz

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

show abstract

“…The classification layer gets the benefit of a strong point of the SVM classifier technique. Oyewola et al [ 19 ] present a new DL technique named data augmentation CNN (DACNN), trained by reinforcement learning (RL) for tackling this issue. The performance of the presented DACNN technique is related to CNN and directed acyclic graph CNN (DAGCNN) techniques.…”

Section: Related Workmentioning

confidence: 99%

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Dutta

Mageswari²,

Gayathri

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.

show abstract

A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

Cited by 37 publications

References 43 publications

Reducing data dimension boosts neural network-based stage-specific malaria detection

Reducing data dimension boosts neural network-based stage-specific malaria detection

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Contact Info

Product

Resources

About