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

Dutta, Ashit Kumar; Mageswari, R. Uma; Gayathri, A.; Bruxella, J. Mary Dallfin; Ishak, Mohamad Khairi; Mostafa, Samih M.; Hamam, Habib

doi:10.1155/2022/7776319

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…Therefore, the challenges of diagnos-ing malaria red blood cells using deep and transfer learning 83 techniques remain the detection accuracy, the diagnostic time 84 frame and the process computational cost. In this sense, 85 various recent works are focused on the data preprocessing 86 step to perform the malaria detection [20], [21].…”

Section: Recently Numerous Deep Learning Approaches Based Onmentioning

confidence: 99%

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

et al. 2022

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This work aims to classify malaria infected cells from those uninfected using two deep learning approaches. Plasmodium parasite transmitted by a female anopheles's mosquitoes bite is the main cause of malaria. Commonly, Microbiological analyses by a microscope allows detecting cells infected from a blood sample, followed by an specialist interpretation of results to conclude the diagnosis process. Taking advantage of efficient deep learning approaches applied in computer vision field, the present framework propose two deep learning architecture based on Recurrent neural Networks to detect accurately malaria infected cells. The first one implements a Convolutional Long Short-Term Memory while the second uses a Convolutional Bidirectional Long Short-Term Memory architecture. A malaria's public dataset consisting of parasitized and uninfected cell images was used for training and testing the proposed model. The methods developed in this work achieved an accuracy of 99.89 % in the detection of malaria cells infected, without preprocessing data. INDEX TERMS Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Malaria 14 inal and chest pain, and the death [2], [3]. Untreated malaria 15 patients may develop long-term pneumonia, anemia, yellow 16 fever, respiratory or brain disorders (Cerebral Malaria) [4].17The World Health Organization (WHO) is deploying 18 strategies for the prevention, treatment, elimination, and 19 surveillance of Malaria to face this global pandemic [1], 20 starting with the diagnosis of the disease. Basically, micro-21 scopic diagnosis method detecting Malaria consist in a mi-22 crobiological analyses using peripheral blood slides [5], [6]. 23 Collection of blood smears for manual microscopic analysis 24 remains an effective method in the diagnosis of Malaria, 25 compared to other methods such as polymerase chain reac-26 VOLUME 4, 2016

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Section: Recently Numerous Deep Learning Approaches Based Onmentioning

confidence: 99%

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

et al. 2022

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“…ML has been widely used in the medical context using clinical data to detect patterns and/or predict different diseases, solving classification problems: using extreme learning machines on malaria parasite detection and classification [ 19 ]; using deep learning and image processing in diabetic retinopathy [ 20 ]; using Internet of Things (IoT) to provide an intelligent forensic analysis [ 21 ]; using FastAI and 1-Cycle Policy in breast cancer metastasis prediction [ 22 ]; using various multimodal models such as decision tree, logistic regression or random forest, among others, in Alzheimer’s disease progression detection [ 23 ]. The ML implementations provide information for the analyst, which can be used to perform a pre-diagnosis if the patient has a particular disease or in identifying significant features; these results as the forms of predictions or classifiers can be ratified by a medical professional, and the professional can give approval to validate or discard this pre-diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

et al. 2022

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According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: “Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal” (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214–0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: “Cer(d18:1/24:1) i2”, “PC(20:3-OH/P-18:1)”, “Ganoderic acid C2”, “TG(16:0/17:1/18:1)” and “GPEtn(18:0/20:4)”.

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“…In recent years, convolutional neural networks (CNNs) have made substantial progress in the field of computer vision and are widely used in medical image classification [ 12 , 13 ], segmentation, registration, reconstruction, and object detection [ 14 , 15 , 16 ]. Due to the powerful feature extraction ability of CNNs, CNN-based deep learning models have been applied to colonoscopies to identify a variety of diseases in the small intestine [ 17 ] and detect polyps [ 18 ], significantly reducing the workload of endoscopists.…”

Section: Introductionmentioning

confidence: 99%

PHF3 Technique: A Pyramid Hybrid Feature Fusion Framework for Severity Classification of Ulcerative Colitis Using Endoscopic Images

Ruan

Liu

et al. 2022

Bioengineering

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Evaluating the severity of ulcerative colitis (UC) through the Mayo endoscopic subscore (MES) is crucial for understanding patient conditions and providing effective treatment. However, UC lesions present different characteristics in endoscopic images, exacerbating interclass similarities and intraclass differences in MES classification. In addition, inexperience and review fatigue in endoscopists introduces nontrivial challenges to the reliability and repeatability of MES evaluations. In this paper, we propose a pyramid hybrid feature fusion framework (PHF3) as an auxiliary diagnostic tool for clinical UC severity classification. Specifically, the PHF3 model has a dual-branch hybrid architecture with ResNet50 and a pyramid vision Transformer (PvT), where the local features extracted by ResNet50 represent the relationship between the intestinal wall at the near-shot point and its depth, and the global representations modeled by the PvT capture similar information in the cross-section of the intestinal cavity. Furthermore, a feature fusion module (FFM) is designed to combine local features with global representations, while second-order pooling (SOP) is applied to enhance discriminative information in the classification process. The experimental results show that, compared with existing methods, the proposed PHF3 model has competitive performance. The area under the receiver operating characteristic curve (AUC) of MES 0, MES 1, MES 2, and MES 3 reached 0.996, 0.972, 0.967, and 0.990, respectively, and the overall accuracy reached 88.91%. Thus, our proposed method is valuable for developing an auxiliary assessment system for UC severity.

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

Cited by 14 publications

References 23 publications

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

PHF3 Technique: A Pyramid Hybrid Feature Fusion Framework for Severity Classification of Ulcerative Colitis Using Endoscopic Images

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