A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.
The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cell...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2381 - 2412 |
|---|---|
| Main Authors: | , , , , |
| Format: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278952&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278952 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278952 187278952 187278952 10.1007/s10278-024-01311-7 187278952 ppf: 2381 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation. aug: au: Pandiaraj, A. Kshirsagar, Pravin R. Thiagarajan, R. Tak, Tan Kuan Sivaneasan, B. affil: https://ror.org/050113w36 Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, 603203, Kattankulathur, Chennai, Tamil Nadu, India sug: subj: Malaria Diagnosis Conceptual Framework Convolutional Neural Networks Deep Learning Diagnostic Imaging Human Algorithms Predictive Value of Tests Prediction Models Simulations ROC Curve Reliability ab: The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells. However, it might not be able to identify infections with small amounts of samples. In the microscopic detection model, blood stains are placed under a microscope for diagnosing malaria. But accurate diagnosis is hard in this method, particularly in developing nations where the disease is most common. The microscopic detection processes are expensive and time-consuming due to the usage of microscopes. The quality of the blood smears and the availability of a qualified specialist, who is skilled in recognizing the disease, impact the accuracy of malaria detection results. The traditional deep learning-based malaria identification models need more processing power. Therefore, a deep learning-based adaptive method is designed to detect malaria cells through the medical image. Hence, the images are gathered from the standard sites and then fed to the segmentation process. Here, the abnormality segmentation is carried out with the help of a developed Trans-MobileUNet + + (T-MUnet + +) network. Trans-MobileUNet + + captures global context, so it is well-suited for segmentation tasks. The segmented image is applied to the adaptive detection phase where the Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model is designed for the effective recognition of malaria cells. The efficiency of the designed approach is elevated by optimizing the parameters from the AA-CRMV2 network with the help of the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm. Several experimental analyses are evaluated in the implemented model over classical techniques to display their effectualness rate. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|