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A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with

A Pandiaraj1, Pravin R Kshirsagar2, R Thiagarajan3

  • 1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India. pandi.mnmjain@gmail.com.

Journal of Imaging Informatics in Medicine
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning method enhances malaria detection from medical images. This adaptive approach improves accuracy and efficiency over traditional methods, aiding in early diagnosis of this mosquito-borne disease.

Keywords:
Adaptive and Atrous Convolution-based Recurrent MobilenetV2Malaria cell segmentation and detectionTrans-MobileUNet + +Updated Random Parameter-based Fennec Fox Optimization

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Area of Science:

  • Medical Imaging
  • Parasitology
  • Artificial Intelligence

Background:

  • Malaria, a deadly mosquito-borne disease, requires early detection for effective treatment.
  • Current diagnostic methods like Rapid Diagnostic Tests (RDTs) and microscopy have limitations in accuracy, cost, and accessibility, especially in endemic regions.
  • Existing deep learning models for malaria detection often demand significant computational resources.

Purpose of the Study:

  • To develop an advanced deep learning-based adaptive method for accurate malaria cell detection in medical images.
  • To overcome the limitations of traditional and current deep learning approaches in terms of accuracy, processing power, and cost.
  • To improve early diagnosis capabilities for malaria.

Main Methods:

  • A novel adaptive deep learning framework was designed, incorporating image segmentation and cell recognition.
  • Abnormality segmentation was performed using a developed Trans-MobileUNet++ (T-MUnet++) network, leveraging its global context capture for segmentation tasks.
  • Malaria cell recognition was achieved using an Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model.
  • The AA-CRMV2 model's parameters were optimized using the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm.

Main Results:

  • The developed Trans-MobileUNet++ effectively segmented abnormalities in medical images.
  • The AA-CRMV2 model, optimized by URP-FFO, demonstrated high efficacy in recognizing malaria cells.
  • Experimental analyses showed the proposed adaptive deep learning approach outperformed classical techniques in malaria detection.

Conclusions:

  • The proposed deep learning-based adaptive method offers a promising, accurate, and efficient solution for malaria detection from medical images.
  • This approach has the potential to enhance early diagnosis, particularly in resource-limited settings.
  • The integration of advanced segmentation and optimized recognition models represents a significant advancement in automated malaria diagnosis.