Detection of Pneumonia Clouds From Chest X-ray Images

Suganthi, L.; Nirmala, K.; Deepa, S; Nagalakshmi, K.; Santhya, M.

doi:10.1007/978-981-15-8530-2_64

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…Apart from the physiological features, several statistical features are also used to build the proposed model. The details of the statistical features can be seen in [86].…”

Section: Data Representationmentioning

confidence: 99%

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Rehman

Shafique

Khan

et al. 2022

Sensors

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This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

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“…Apart from the physiological features, several statistical features are also used to build the proposed model. The details of the statistical features can be seen in [86].…”

Section: Data Representationmentioning

confidence: 99%

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Rehman

Shafique

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly as expected by the model [14]. By using a radius of 3, Suganthi et al modified the contrast of the image, and applied morphological operations to discard the irrelevant bone parts and dilation to smooth the lung boundary [51]. Later, these preprocessed chest X-ray images are used for the segmentation process as per region of interest using K-means clustering [51].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Suganthi et al extracted gray level co-occurrence matrix (GLCM) features from preprocessed X-ray images and selected dominant features using Analysis of variance (ANOVA) tests [51]. Similarly to the histogram calculation, pixel pair values are selected and the count is incremented by 1 at that pixel location in the GLCM matrix.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…The entire image is segmented into smaller chunks of 8×8, 16×, and 32× and a GLCM matrix is generated for each chunk. In this step, GLCM features such as correlation, homogeneity, energy, entropy, and contrast are calculated [51]. Shabnam and Mohamed suggested using the fuzzy C-means algorithm instead of discrete wavelet transforms, wavelet frame transforms, and wavelet packet transforms, etc.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Deep learning has the advantage that researchers don't have to work on feature extraction. The set of Xray images are fed to convolutional neural networks that convert the two-dimensional Support Vector Machine (SVM) classifiers in order to detect pneumonia [51].…”

Section: Classification Limitations Of Existing Workmentioning

confidence: 99%

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