Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction

Erten, Mehmet; Tuncer, Ilknur; Barua, Prabal Datta; YILDIRIM, Kübra; Doğan, Şengül; Tuncer, Türker; Tan, Ru‐San; Fujita, Hamido; Acharya, U. Rajendra

doi:10.1007/s10278-023-00827-8

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…CNNs are widely used in biomedical imaging because it gives promising results in detection and classification of different disease. 9,33,34 DL models such as CNN can be very efficient, inexpensive, and scalable compared to deep pre-trained models. Deep models can provide maximum performance, but they have some drawbacks such as overfitting problems and a large number of layers making them computationally expensive and requiring longer training time.…”

Section: Introductionmentioning

confidence: 99%

“…DL models have the ability to process large datasets, which brings revolution in medical image analysis and therefore improves accuracy and efficiency. CNNs are widely used in biomedical imaging because it gives promising results in detection and classification of different disease 9,33,34 . DL models such as CNN can be very efficient, inexpensive, and scalable compared to deep pre‐trained models.…”

Section: Introductionmentioning

confidence: 99%

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MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images

Asif,

Khan,

Zheng

et al. 2023

Int J Imaging Syst Tech

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Our study presents MozzieNet, a customized CNN model aimed at improving the identification of malaria parasites in blood smear microscopic images. By optimizing hyperparameters and incorporating techniques like data augmentation, batch normalization, and dropout, our model enhances robustness and generalization, addressing overfitting issues. Using the open‐source NIH malaria dataset with 27,558 images, we achieve a classification accuracy of 96.73%, recall rate of 97.90%, precision of 95.67%, area under the curve (AUC) of 99.35%, and F1 score of 96.77%. We performed feature maps and Grad‐CAM analysis on our proposed MozzieNet model to visualize and examine the targeted regions that are crucial for accurate predictions. Statistical analysis shows that the proposed architecture achieves promising performance and is superior to pre‐trained models and existing methods for malaria detection. MozzieNet is designed for cloud and low‐end smartphones, enabling malaria diagnosis in remote areas, thereby assisting physicians in informed malaria diagnosis and decision‐making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images

Asif,

Khan,

Zheng

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

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Automated urine sediment analyzers play a crucial role in diagnosing urinary tract infections, offering real-time data analysis and expediting patient diagnosis. This paper introduces a novel hybrid approach combining data-centric and model-centric techniques for automated urine sediment analysis. The proposed methodology addresses challenges such as morphological similarities among particle classes, uneven particle distribution, and intra/inter-class variations. A modified version of convolutional neural network (CNN), specifically the Visual Geometry Group (VGG-19) model, incorporating transfer learning, along with data augmentation is proposed for automated urine sediment classification with 98% accuracy and impressive inference time of 61ms per image. The proposed approach outperforms existing methods, especially in handling diverse sediment categories, demonstrating its potential for practical applications in medical diagnostics. We proposed the integration of a data-centric approach for improved labeling reliability and a model-centric approach for fine-tuning of the deep learning model, showcasing promising results in recognizing 12 distinct urine sediment classes. This study also emphasizes the importance of collaboration with medical professionals in refining the model's performance and handling challenges related to data acquisition and class imbalance. The proposed approach provides a significant advancement in automating and enhancing urine sediment analysis processes.

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Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction

Cited by 2 publications

References 39 publications

MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images

MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

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