A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images

Nahiduzzaman, Md.; Goni, Md. Omaer Faruq; Anower, Md. Shamim; Islam, Md. Robiul; Ahsan, Mominul; Haider, Julfikar; Saravanakumar, G.; Hassan, Rakibul; Islam, Md. Rakibul

doi:10.1109/access.2021.3123782

Cited by 43 publications

(20 citation statements)

References 36 publications

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“…For this reason, using feature selection or feature reduction steps before classification instead of using direct CNN models with many features can increase the efficiency of the network. To create a feature vector that better represents the output data, either useful features are selected, or different transformations are applied to existing features [ 62 , 63 ]. The most well-known method for size reduction is PCA.…”

Section: Methodsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.

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Section: Methodsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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“…And the weight between the hidden and output layer is (None, 1) 0 calculated analytically using the Moore-Penrose Pseudoinverse method. The ELM architecture is simple and does not have iterative parameter tuning that makes the training process faster and achieves adequate performance in disease classification [42], [43]. Fig.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

et al. 2023

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Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests. However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed. The parasite inflator acts as the shape increaser of the small, darker malaria parasites in the RBC images in order to detect malaria easily. CNN-DELM has achieved a sanguine result on every performance standard compared to CNN and CNN-ELM. The proposed CNN-DELM method has achieved 97.79% and 99.66% accuracy for the original version and the modified version, respectively. Hence, the proposed CNN-DELM model has also produced either comparable or better results when compared to other methods proposed in the literature, showing its robustness in detecting malaria.

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“…A depth-separable convolution can isolate the most efficient section of a standard convolution while rejecting the rest It achieved an accuracy of 94.87%, a sensitivity of 96.70, a specificity of 91.90, and an AUC of 98.80 When the magnitude of Gaussian noise rose, its performance dropped substantially 37 This effort involved the categorization of three classes. It utilized VGG19 and transfer learning techniques using weights that were previously trained It achieved 97.11% accuracy, medium precision, and 97% recall The employed dataset is modest in size 38 Proposed an ensemble of ChexNet and VGG19, and retrieved features were used for ensemble classification using the random forest approach Prediction accuracy of 98.93% and AUC 0.99 The model was overfitted by the ensembled architecture's large number of trainable parameters 39 This work used Kaggle CXR images to create the extreme learning machine (ELM). They applied PCA for feature extraction with contrast-enhanced by contrast, restricted adaptive histogram equalization on CXR images the ELM model with CLAHE and hybrid CNN-PCA beat all other models A large dataset is required for better results 40 They suggested a multitask deep learning technique capable of recognizing COVID-19 patients and segmenting COVID-19 lesions from chest CT images simultaneously.…”

Section: Background and Similar Workmentioning

confidence: 99%

Pneumonia detection with QCSA network on chest X-ray

Singh¹,

Kumar²,

Kumar

et al. 2023

Sci Rep

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Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.

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A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images

Cited by 43 publications

References 36 publications

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

Pneumonia detection with QCSA network on chest X-ray

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