Hybrid convolutional neural network and Flexible Dwarf Mongoose Optimization Algorithm for strong kidney stone diagnosis

•A novel methodology for kidney stone diagnosis using CNN and metaheuristic approach.•Image quality enhancement through preprocessing techniques and data augmentation.•A new introduced metaheuristic, termed Flexible Dwarf Mongoose Optimizer (FDMO).•Network weights optimizing using FDMO algorithm, re...

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Veröffentlicht in:Biomedical signal processing and control Jg. 91; S. 106024
Hauptverfasser: Liu, Haozhi, Ghadimi, Noradin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2024
Schlagworte:
Convolutional neural network
CT Kidney Dataset
Flexible Dwarf Mongoose Optimization Algorithm
Kidney stone diagnosis
Metaheuristic approach
Preprocessing techniques
Kidney stone diagnosis
Flexible Dwarf Mongoose Optimization Algorithm
Metaheuristic approach
CT Kidney Dataset
Convolutional neural network
Preprocessing techniques
ISSN:1746-8094, 1746-8108
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Zusammenfassung:•A novel methodology for kidney stone diagnosis using CNN and metaheuristic approach.•Image quality enhancement through preprocessing techniques and data augmentation.•A new introduced metaheuristic, termed Flexible Dwarf Mongoose Optimizer (FDMO).•Network weights optimizing using FDMO algorithm, resulting in improved diagnostic accuracy.•Outperforms existing techniques in diagnosing kidney stones using the “CT Kidney Dataset”. This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol variation in medical imaging, which often leads to poor generalization of deep learning models. The model first uses three preprocessing techniques to enhance the quality of raw images and increase their quantity for effective CNN training. The main idea is to optimize the main arrangement of the convolutional neural network based on the proposed flexible version of dwarf mongoose optimization (FDMO) algorithm to provide a good detector model in kidney stone diagnosis. The model is then trained and tested on images from “CT Kidney Dataset”, and its comparison results with some other published works demonstrates its robustness and ability to generalize. The results indicate a significant improvement in diagnostic accuracy, potentially minimizing physician-induced errors and enhancing patient care.
ISSN:1746-8094
1746-8108
DOI:10.1016/j.bspc.2024.106024