Pneumonia classification using quaternion deep learning

Singh, Sukhendra; Tripathi, Bipin Kumar

doi:10.1007/s11042-021-11409-7

Cited by 27 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Singh¹,

Rawat²,

Gupta³

et al. 2023

CMC

View full text Add to dashboard Cite

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.

show abstract

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

CMC

View full text Add to dashboard Cite

show abstract

“…Among these models, AlexNet demonstrated the highest accuracy of 83.968%. Kavya et al [5] [6] presented an innovative neural network named Quaternion Convolution neural network (QCNN) that considers all three color channels of an image as a unified entity, resulting in enhanced feature extraction and classification performance. The authors trained the Quaternion Residual network using an extensive Chest X-Ray dataset, achieving a remarkable classification accuracy of 93.75% and an F-score of 0.94.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Diagnosis using a Hybrid Framework Leveraging Deep Learning and Machine Learning Techniques

Muralidhar,

Sathyarajasekaran

et al. 2023

Preprint

View full text Add to dashboard Cite

Pneumonia is a potentially fatal lung infection caused by various viral infections. Because pneumonia is like other pulmonary diseases, identifying and treating it on chest X-ray images can be difficult. Therefore, a more reliable method for detecting pneumonia in X-ray images is required. So, an optimized Deep Learning approach is used in this study, in which x-rays of the chest are handled in four stages. The first stage involves performing image enhancement, followed by a data augmentation stage, and the third stage involves feeding the results to deep learning algorithms (CNN, VGG16, InceptionResNetV2, Xception, Resnet50, and hybrid model) in which image features are retrieved for further processing. These extracted features are then utilized in the subsequent step, where they are inputted into various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, SVM, and AdaBoost. These algorithms are responsible for classifying and diagnosing the images. To evaluate the proposed approach, a comprehensive dataset comprising 8,217 images (5,259 pneumonia and 2,958 normal) from combined chest X-ray and Mendeley sources was employed. The findings of the experiment reveal that the hybrid model, combined with the SVM classifier, demonstrates exceptional performance in terms of both training and testing accuracy, exceeding 97.70%. In conclusion, the proposed framework successfully leverages the extracted features and machine learning algorithms to achieve highly accurate classification and diagnosis of pneumonia cases. The hybrid model, in conjunction with the SVM classifier, exhibits remarkable performance in accurately identifying pneumonia from chest X-ray images.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Pneumonia classification using quaternion deep learning

Cited by 27 publications

References 29 publications

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Efficient Pneumonia Diagnosis using a Hybrid Framework Leveraging Deep Learning and Machine Learning Techniques

Automatic Detection of Pneumonia using Concatenated Convolutional Neural Network

Contact Info

Product

Resources

About