Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

Safuan, Syadia Nabilah Mohd; Tomari, Mohd Razali Md; Zakaria, Wan Nurshazwani Wan

doi:10.3991/ijoe.v18i02.27321

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…It is a type of CNN implemented especially for mobile vision systems and in-vehicle applications. The basic idea behind MobileNet is to use deep separable convolutions to compose simpler and lightweight deep neural networks that can have low latency for mobile and embedded devices [23] DenseNet201. It is a type of CNN that uses dense connections between these different layers, through dense blocks, where all the layers are directly connected with each other.…”

Section: Results Comparisonmentioning

confidence: 99%

Transfer Learning in Keratoconus Classification

et al. 2022

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Early detection of keratoconus will provide more treatment choices, avoid heavy treatments, and help stop the rapid progression of the disease. Unlike traditional methods of keratoconus classification, this study presents a machine learning-based keratoconus classification approach, using transfer learning, applied on corneal topographic images. Classification is performed considering the three corneal classes already cited : normal, suspicious and keratoconus. Keratoconus classification is carried out using six pretrained convolutional neural networks (CNN) VGG16, InceptionV3, MobileNet, DenseNet201, Xception and EfficientNetB0. Each of these different classifiers is trained individually on five different datasets, generated from an original dataset of 2924 corneal topographic images. Original corneal topographic images have been subjected to a special preprocessing before their use by different models in the learning phase. Images of corneal maps are separated in five different datasets while removing noise and textual annotation from images. Most of models used in the classification allow good discrimination between normal cornea, suspicious and keratoconus one. Obtained results reached classification accuracy of 99.31% and 98.51% by DenseNet201 and VGG16 respectively. Obtained results indicate that transfer learning technique could well improve performance of keratoconus classification systems.

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Section: Results Comparisonmentioning

confidence: 99%

Transfer Learning in Keratoconus Classification

et al. 2022

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“…Additionally, [2] projected a deep learning-based classification model using the RDO-GDRL network, which [3] utilized CNN for WBC identification and achieved an accuracy of 96.78%. [4] compared different CNN models, where Alexnet outperformed others with high accuracy across multiple datasets. [5] delved into the application of CNN in radiology tasks and discussed future directions and challenges.…”

Section: Related Workmentioning

confidence: 99%

Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

Rajalakshmi,

Senthilkumar

2023

IJRITCC

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The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis.

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“…The average of the values obtained in each division is the performance metric given by the k-fold cross validation. This technique is computationally expensive, but it does not waste a lot of data (unlike defining an arbitrary validation set) and has huge advantages in problems such as inverse inference or in situations where we have a dataset with small number of samples [31].…”

Section: Stratified Cross Validationmentioning

confidence: 99%

Comparative Study of Multiple CNN Models for Classification of 23 Skin Diseases

Aboulmira

Hrimech²,

Lachgar³

2022

Int. J. Onl. Eng.

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Cutaneous disorders are one of the most common burdens world-wide, that affects 30% to 70% of individuals. Despite its prevalence, skin disease diagnosis is highly difficult due to several influencing visual clues, such as the complexities of skin texture, the location of the lesion, and presence of hair. Over 1500 identified skin disorders, ranging from infectious disorders and benign tumors to severe inflammatory diseases and malignant tumors, that often have a major effect on the quality of life. In this paper, several deep CNN architectures are proposed, exploring the potential of Deep Learning trained on “DermNet” dataset for the diagnosis of 23 type of skin diseases. These architectures are compared in order to choose the most performed one. Our approach shows that DenseNet was the most performed one for the skin disease classification using DermNet Dataset with a Top-1 accuracy of 68.97% and Top-5 accuracy of 89.05%.

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Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

Cited by 5 publications

References 20 publications

Transfer Learning in Keratoconus Classification

Transfer Learning in Keratoconus Classification

Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

Comparative Study of Multiple CNN Models for Classification of 23 Skin Diseases

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