Big Data Challenges and Solutions in the Medical Industries

In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.

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“…[48] Blockchain The researchers devised a blockchain access management method for safeguarding big data. [72] NA…”

Section: Blockchain Cryptographymentioning

confidence: 99%

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

2023

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“…These strategies comprised support vector machine (SVM)-based feature descriptor learning, transfer learning, and a combination of the two. These and other tactics were employed to achieve the aim [ 28 , 29 , 30 , 31 ]. Because the DeepLabv3+ and MobileNet designs could be combined, they were able to divide a hard disc.…”

Section: Related Workmentioning

confidence: 99%

Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model

et al. 2022

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Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.

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“…The substantial removal of lung tissue, the vast number of radiographic images, and the complex and uneven structure may make an accurate diagnosis difficult. CADAS is a computer-based system that assists clinicians in diagnosing medical problems [ 1 , 2 ]. These tools provide images of potentially dangerous situations to help radiologists make the best diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

Kumar

Altahan

Rasheed

et al. 2023

Computational Intelligence and Neuroscience

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Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians’ detection rate was 74.60 percent lower than deep learning’s detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

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Big Data Challenges and Solutions in the Medical Industries

Cited by 28 publications

References 48 publications

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

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