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January 22, 2026Computational Intelligence

Classification of Malignant Lymphoma Using Interpretable Dilated MobileNetV2 With Convolutional Recurrent Neural Network

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Authors

PGPriyaa Sri GanesanSree Balaji Dental College and HospitalAKAnanthajothi KaliyamoorthyRajalakshmi Engineering College

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Implication

Developed a deep learning model for classifying malignant lymphoma, indicating higher accuracy than conventional methods.

Key Points

  • The study aims to enhance the classification accuracy of malignant lymphomas using deep learning techniques.
  • Utilized a deep learning model combining Dilated MobileNetV2 and Convolutional-Recurrent Neural Network.
  • Images were processed using Hybrid Adaptive and Attentive Networks.
  • Tuned network parameters using Revised Iteration-based Peregrine Falcon Optimization.
  • Compared the new model's performance against traditional classification methods.
  • Achieved classification accuracies of 93.67%, 94.74%, and 95.36% with different activation functions.
  • Proved higher accuracy than baseline classification mechanisms.

Cite This Study

Ganesan et al. (2026) studied this question.

synapsesocial.com/papers/6971bdcf642b1836717e27a2https://doi.org/10.1111/coin.70173
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