Detection of Lung Diseases using Deep Learning

Naik, Rasika; Wani, Tejas; Bajaj, Sakshi; Ahir, Shiva; Joshi, Atharva

doi:10.2139/ssrn.3568730

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Radiologists could utilize the technique that Naik et al [112] demonstrated to identify lung diseases. The DenseNet-121 model is used by the suggested system to process the chest X-ray pictures.…”

Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

“…The efficacy of deep learningbased medical image analysis is demonstrated by a variety of recently published literature [12,[37][38][39][40][41]49,[49][50][51][52][53][54][55][56][57][58][59][60][61][62]64,64,65,[157][158][159]. In the literature, several deep learning architectures have been described to handle various imaging modalities and tasks related to medical image processing with different components of encryption or cryptography [107,110,[112][113][114][115][116]123,124,135,136,140,141,144,[147][148][149][150][151]160,161]. ...…”

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

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Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.

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Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

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“…Fig.4b. Mobile/Desktop Application to detect the diseases [3] Therefore, in the case of immunocompromised patients, detection of lung disease, and most importantly pneumonia and pneumothorax, is necessary, as coronavirus is a potential causative organism of pneumonia. Hence, it is undoubtedly the best diagnostic method which governs and dominates the biomedical industry and clinical practice.…”

Section: Fig4a Steps Involved In Creating a Machine Learning Modelmentioning

confidence: 99%

The impact of Artificial Intelligence, Blockchain, Big Data and evolving technologies in Coronavirus Disease - 2019 (COVID-19) curtailment

Ahir¹,

Telavane²,

Thomas³

2020

2020 International Conference on Smart Electronics and Communication (ICOSEC)

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The pandemic of Coronavirus Disease 2019 (COVID-19) is proliferating across the globe obnoxiously and it is the most heard buzzword in recent times. Every person ranging from older people, persons with disabilities, youth, indigenous people have become a part of this chain and are most likely to suffer in the upcoming chronology. S ocial distancing is likely to become a new norm where "Work from Home'', Online Lectures" and "Meetings" ensue on social media applications. Technology has always lent a helping hand for mankind's problems. The idea focuses on highlighting the advancements in technology in the midst of a bizarre situation. Deep Learning applications to detect the symptoms of COVID-19, AI based robots to maintain social distancing, Blockchain technology to maintain patient records, Mathematical modeling to predict and assess the situation and Big Data to trace the spread of the virus and other technologies. These technologies have immensely contributed to curtailing this pandemic. S trong will power, patience and optimistic guidelines catered by the respective government are some of the altercations to COVID-19.

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“…COVID-19 has expanded swiftly by person-to-person transfer, wreaking havoc on world health. COVID-19 infected around 16.89 million people worldwide, resulting in close to 663,476 deaths [1]. Because of the scarcity of intensive care units (ICUs), all countries' healthcare systems have collapsed.…”

Section: Introductionmentioning

confidence: 99%

Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray

Khadhim

Kadhim²,

Shams³

et al. 2023

IJEECS

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<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>

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Detection of Lung Diseases using Deep Learning

Cited by 6 publications

References 16 publications

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Deep Learning for Medical Image Cryptography: A Comprehensive Review

The impact of Artificial Intelligence, Blockchain, Big Data and evolving technologies in Coronavirus Disease - 2019 (COVID-19) curtailment

Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray

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