Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Bozkurt, Ferhat

doi:10.53525/jster.1014186

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…DenseNet is a neural network model that is proposed to solve the problem with vanishing gradient. Vanishing gradient often happens with deep architectures, DenseNet solved this by densely connecting layers in order to prevent the gradient loss during backpropagation in the early layers (Bozkurt, 2021). Figure 5 show the architectures of the model used in this study.…”

Section: Pretrained Cnn Modelmentioning

confidence: 99%

Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images

Parmonangan,

Marsella,

Pardede

et al. 2023

EMACS Journal

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Melanoma is a kind of rare skin cancer that can spread quickly to the other skin layers and the organs beneath. Melanoma is known to be curable only if it is diagnosed at an early stage. This poses a challenge for accurate prediction to cut the number of deaths caused by melanoma. Deep learning methods have recently shown promising performance in classifying images accurately. However, it requires a lot of samples to generalize well, while the number of melanoma sample images is limited. To solve this issue, transfer learning has widely adapted to transfer the knowledge of the pretrained model to another domain or new dataset which has lesser samples or different tasks. This study is aimed to find which method is better to achieve this for early melanoma prediction from skin lesion images. We investigated three pretrained and one non-pretrained image classification models. Specifically, we choose the pretrained models which are efficient to train on small training sample and low hardware resource. The result shows that using limited sample images and low hardware resource, pretrained image models yield better overall accuracy and recall compared to the non-pretrained model. This suggests that pretrained models are more suitable in this task with constrained data and hardware resource.

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Section: Pretrained Cnn Modelmentioning

confidence: 99%

Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images

Parmonangan,

Marsella,

Pardede

et al. 2023

EMACS Journal

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An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

Rao

2023

IEEE Access

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White Blood Cells are essential in keeping track of a person's health. However, the pathologist's experience will determine how the blood smear is evaluated. Furthermore, it is still challenging to classify WBCs accurately because they have various forms, sizes, and colors due to distinct cell subtypes and labeling methods. As a result, a powerful deep learning system for WBC categorization based on MobilenetV3-ShufflenetV2 is described in this research. Initially, the WBC images are segmented using an efficient Pyramid Scene Parsing Network (PSPNet). Following that, MobilenetV3 and an Artificial Gravitational Cuckoo Search (AGCS)-based technique are used to extract and select the global and local features from the segmented images. Finally, the WBC images are divided into five classes using a ShufflenetV2 model. The proposed approach is evaluated on blood cell count and detection (BCCD) and Raabin-Wbc datasets and achieves 99.19% and 99% accuracy, respectively. Moreover, the results are satisfactory when compared to existing algorithms. INDEX TERMS white blood cells, deep learning, MobilenetV3, ShufflenetV2

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Blood Cell Image Recognition Algorithm based on EfficientNet

Guan

Wang

2022

2022 IEEE International Conference on Mechatronics and Automation (ICMA)

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Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Cited by 5 publications

References 21 publications

Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images

Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images

An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

Blood Cell Image Recognition Algorithm based on EfficientNet

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