PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Bhosale, Yogesh H.; Patnaik, Kiran Kumar

doi:10.1016/j.bspc.2022.104445

Cited by 54 publications

(21 citation statements)

References 39 publications

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“…Several recent techniques in ML have been applied to predict or diagnose diseases [ 61 , 62 ]. Natural Language Processing (NLP) techniques have been used to analyze electronic health records (EHRs) to extract information that can be used to predict or diagnose diseases [ 63 ].…”

Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

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A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.

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Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

View full text Add to dashboard Cite

show abstract

“…Moreover, the fusion of voice signals with signals of other modalities such as electroacoustic gate signals, EMR, X-ray images, and ultrasound [ 4 , 5 ] will be more valuable for disease diagnosis in smart hospitals. For example, combining the chest X-ray images and cough sounds-based COVID-19 non-contact classification methods will minimize severity and mortality rates during the pandemic [ 5 , 6 , 188 , 189 ]. Furthermore, algorithms in other domains can be used in speech signal processing, such as AlexNet, VGGNet, GoogLeNet, and ResNet in image recognition can be adopted in the spectrum of speech signals.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…For example, a patient with multiple diseases should be treated by doctors first. At the same time, IST combined with image processing [ 6 ] and other technologies can assist doctors in their work to address the uncertainties and improve the robustness and acceptability of AI-based medical systems. In most cases, with techniques such as semantic reasoning and knowledge mapping, machines can better understand the intentions of the speaking of doctors and patients.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…With the development of robotics and artificial intelligence (AI) technologies, machines can achieve more efficient and accurate disease diagnosis and assessment in some cases and replace nurses to assist patients in their lives, which alleviate the problem of insufficient medical resources. For example, intelligent image processing methods based on deep learning (DL) have been applied to processing X-ray, CT, ultrasound, and facial images for diagnosing diseases such as COVID-19 detection [ [4] , [5] , [6] ], paralysis assessment [ 7 , 8 ], and autism screening [ 9 ]. In addition, intelligent speech technology (IST) plays a critical role in smart hospitals because language is the most natural mean of communication between doctors and patients and contains much information, such as patients’ identity, age, emotion, and even symptoms of diseases [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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“…Bhosale et al . [ 37 – 41 ] had proposed the applications of deep learning techniques in diagnosis of COVID-19 and deep convolutional neural network based COVID-19 classification from radiology X-ray images for IoT enabled devices. Table 1 is a summary of image inpainting methods based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

RNON: image inpainting via repair network and optimization network

Chen

Xia

Yang

2023

Int. J. Mach. Learn. & Cyber.

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In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon. The paper has proposed an effective image inpainting method using generative adversarial networks, which is composed of two mutually independent generative confrontation networks. Among them, the image repair network module aims to solve the problem of repairing the irregular missing areas of the image, and its generator is based on a partial convolutional network. The image optimization network module aims to solve the problem of local chromatic aberration in the repaired images, and its generator has based on deep residual networks. Through the synergy of the two network modules, the visual effect and image quality of the images has improved. The experimental results can show that the proposed method (RNON) performs better from comparisons of qualitative and quantitative evaluations with state-of-the-arts in image inpainting quality field.

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PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Cited by 54 publications

References 39 publications

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

RNON: image inpainting via repair network and optimization network

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