Hybridizing Convolutional Neural Network for Classification of Lung Diseases

Soni, Mukesh; Gomathi, S.; Kumar, Pankaj; Churi, Prathamesh; Mohammed, Mazin Abed; Salman, Akbal Omran

doi:10.4018/ijsir.287544

Cited by 26 publications

(12 citation statements)

References 53 publications

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“…In particular, 80% of the data from the collected regions are utilized for training, and 20% are used for testing. After gathering the medical images, they are processed through the steps above, and the methods are implemented using the uniform platform [34]. Here, NVIDIA GTX1060Ti, Intel i7 Core processors, and MATLAB are utilized to develop the system.…”

Section: Resultsmentioning

confidence: 99%

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Jaber¹,

Yussof²,

Elameer³

et al. 2022

Computers, Materials &Amp; Continua

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Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis. The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule. The pattern-matching process recognizes the disease effectively, and the system's efficiency is evaluated using the MATLAB implementation process. This process ensures high accuracy of up to 99.02% to 99.37% and reduces computational complexity.

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Section: Resultsmentioning

confidence: 99%

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Jaber¹,

Yussof²,

Elameer³

et al. 2022

Computers, Materials &Amp; Continua

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“…Other approaches that have reported the use and evaluated the performance of deep learning algorithms in the diagnosis of COVID-19 applications with X-ray images include the use of space transformer network (STN) with CNNs by Soni et al. [36] , Alqudah, Qazan and Alqudah [37] , Chakraborty, Dhavale and Ingole [38] , Karakanis and Leontidis [39] , Bekhet et al. [40] ; Huang and Liao [41] developed lightweight CNNs using Chest X-ray images based COVID-19 detection.…”

Section: State-of-the-artmentioning

confidence: 99%

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Ogechukwu¹,

Hindy²,

Ukwandu³

2022

Healthcare Analytics

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“…In the last decade of computing science, deep learning was gaining monumental traction, with neural networks leading the way. Numerous research works made important contributions in fields, such as the classification of lung and breast diseases [ 9 , 10 ] and the identification and segmentation of gynecological abnormality [ 11 , 12 ]. Since Goodfellow and others proposed generative adversarial networks (GANs) in 2014 [ 13 ], GANs were shown a promising future in building image synthesis and data generation techniques.…”

Section: Introductionmentioning

confidence: 99%

AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network

et al. 2022

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Problem: Ultrasonography is recommended as the first choice for evaluation of thyroid nodules, however, conventional ultrasound features may not be able to adequately predict malignancy. Ultrasound elastography, adjunct to conventional B-mode ultrasound, can effectively improve the diagnostic accuracy of thyroid nodules. However, this technology requires professional elastography equipment and experienced physicians. Aim: in the field of computational medicine, Generative Adversarial Networks (GANs) were proven to be a powerful tool for generating high-quality images. This work therefore utilizes GANs to generate ultrasound elastography images. Methods: this paper proposes a new automated generation method of ultrasound elastography (AUE-net) to generate elastography images from conventional ultrasound images. The AUE-net was based on the U-Net architecture and optimized by attention modules and feature residual blocks, which could improve the adaptability of feature extraction for nodules of different sizes. The additional color loss function was used to balance color distribution. In this network, we first attempted to extract the tissue features of the ultrasound image in the latent space, then converted the attributes by modeling the strain, and finally reconstructed them into the corresponding elastography image. Results: a total of 726 thyroid ultrasound elastography images with corresponding conventional images from 397 patients were obtained between 2019 and 2021 as the dataset (646 in training set and 80 in testing set). The mean rating accuracy of the AUE-net generated elastography images by ultrasound specialists was 84.38%. Compared with that of the existing models in the visual aspect, the presented model generated relatively higher quality elastography images. Conclusion: the AUE-net generated ultrasound elastography images showed natural appearance and retained tissue information. Accordingly, it seems that B-mode ultrasound harbors information that can link to tissue elasticity. This study may pave the way to generate ultrasound elastography images readily without the need for professional equipment.

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Hybridizing Convolutional Neural Network for Classification of Lung Diseases

Cited by 26 publications

References 53 publications

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network

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