A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification

Pandey, Sanat Kumar; Bhandari, Ashish Kumar

doi:10.1007/s11831-023-09940-x

Cited by 7 publications

(4 citation statements)

References 122 publications

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“…As a result, 1200 brain landmarks from MRIs while 7 prostate landmarks from CT images were detected. The goal of LUNA16 [53] challenge is to improve the ability to detect lung nodules on CT scans, which is essential for pulmonary cancer diagnosis [54]. On the same LUNA 16 dataset, deep ensemble 2D CNN approach [55] comprising three different CNNs with different layers, pooling techniques and kernels is applied and it predicted the outcome with more accuracy.…”

Section: Detectionmentioning

confidence: 99%

“…In order to protect the confidentiality and integrity of medical image analysis, deep learning architectures with security features are essential. 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]…”

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

confidence: 99%

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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: Detectionmentioning

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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“…In Lung cancer detection term, received image require to be pre-processed and detect whether suspected areas are tumor/not. While cells in lung tissues are raised in the uncontrolled way that leads in generating disease of lung cancer [2]. With no treatment, the growth could move to the other close tissues that known as metastasis.…”

Section: Introductionmentioning

confidence: 99%

“…Lung cancer diagnosis is too essential, due to that early disease diagnosis could cause in better treatment. That is too hard task for a doctor, since a lot of factors exist influencing decision on cancer kinds' diagnosis [2].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer classification using an ensemble of CNNs Method in CT Scan Images

Betti,

Naser,

Tanha

et al. 2024

JEPS

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About five million people lose their lives every year to lung cancer, making it one of the leading causes of mortality worldwide. In the last few years, a lot of methods of detection of lung cancer were improved however these could not efficiently diagnose cancer. In this paper, a convolutional neural network (CNN) of robust deep learning is developed. CNN precision raises the deeper that is, however, it causes over-ﬁtting or vanishing gradient problems simultaneously. To solve the issue, the CNN used resort to parallel CNN. It used Pictures the LIDC-IDRI consortium image collection has thoracic CT images that have been annotated for lung cancer diagnosis and screening purposes. The presented model includes result models of CNN which are integrated via ensemble methods and it compare with every model. Results of the simulation illustrate that the presented method's accuracy has developed by 2.18 percent in comparison with the method of the main paper

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