Pneumonia classification using quaternion deep learning

Singh, Sukhendra; Tripathi, Bipin

doi:10.1007/s11042-021-11409-7

Cited by 9 publications

(4 citation statements)

References 29 publications

(4 reference statements)

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“…Various architectures were used as transfer learning models. Their weights were trained on an ImageNet dataset and input shape was changed to (50,50,3) (the size of the image of our dataset). We imported only the CNN architectures of the models and excluded the fully connected layers.…”

Section: Transfer Learning With Traditional Machine Learning Algorithmmentioning

confidence: 99%

“…Researchers from all over the world are actively collaborating and attempting to create early detection methods for breast cancer, and to solve this issue, various cutting-edge technologies have contributed in this arena, reducing the mortality rate due to breast cancer [3]. Because of their structure, size, and location, automatically locating and identifying cancer cells is a difficult challenge today.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Singh¹,

Rawat²,

Gupta³

et al. 2023

CMCHas correction 2023-1-5

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Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.

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Section: Transfer Learning With Traditional Machine Learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Singh¹,

Rawat²,

Gupta³

et al. 2023

CMCHas correction 2023-1-5

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“…Singh et al [25] built a modi ed model of ResNet, called Quaternion CNN. The proposed model differs from the original ResNet model in which every convolutional layer is replaced by a Quaternion convolutional layer, also every Relu layer is replaced by an exponential linear unit.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Pneumonia using Concatenated Convolutional Neural Network

Al-Taani

Al-Dagamseh

2022

Preprint

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Pneumonia is a life-threatening disease and early detection can save lives, many automated systems have contributed to the detection of this disease and currently deep learning models have become one of the most widely used models for building these systems. In this study, two deep learning models are combined: DenseNet169 and pre-activation ResNet models and used for automatic detection of pneumonia. DenseNet169 model is an extension of the ResNet model, while the second is a modified version the ResNet model, these models achieved good results in the field of medical imaging. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset consists of 5856 images. Achieved results were promising compared to several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97%, and a loss value of 0.23.

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“…Complex features are extracted from input images and further categorized in the architecture's final levels. Transfer learning has been applied with pre-trained classifiers [12,13] with better performance. Although the attention mechanism [14][15][16][17] was primarily used for language processing-related tasks like image captioning [18][19][20][21][22], language translation [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Singh¹,

Rawat²,

Gupta³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

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In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.

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Pneumonia classification using quaternion deep learning

Cited by 9 publications

References 29 publications

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Automatic Detection of Pneumonia using Concatenated Convolutional Neural Network

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

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