Deep Learning: An Application Perspective

Sakshi, .; Das, Prasenjit; Jain, Shaily; Sharma, Chetan; Kukreja, Vinay

doi:10.1007/978-981-16-4284-5_28

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The application of deep learning is not a new concept [29], [32]. Since it began and has grown quickly, many sub-fields of healthcare are being studied, and many new ways are being found to solve the problems that still exist in the medical world.…”

Section: Resultsmentioning

confidence: 99%

Neural Network Based Approach to Diagnose and Classify Monkeypox Disease

Sakshi,

Lodhi,

Kukreja

2023

IJCDS

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Numerous organizations, including local governments, health, and medical institutions, and even the WHO, have expressed concern about the fatality and transmission rates of this illness due to the rapid increase in monkeypox cases around the world. Monkeypox illness and the COVID-19 virus have a very similar pattern of spread. The number of reported cases worldwide has increased in recent months, continuing the same trend as COVID-19. The characteristics and signs of the monkeypox virus are similar to those of any other viral illness. The main signs and symptoms are fever, chills, fatigue, headache, etc. At this juncture, the diagnosis is a significant challenge. Another challenge is posed by the disease when the obvious symptoms occur like rashes on the skin. The problem with these specific symptoms is that their appearance resembles other diseases like Chickenpox, Cowpox, and so on. Thus, the correct classification and proper diagnosis of the disease are tough and strenuous. Thus, for efficient and correct classification of this severe epidemic, a neural network-based solution is proposed, which classifies the disease at its initial stage with a competent accuracy rate of 94.38%. the proposed solution is excelling the diagnosis problem by performing efficient classification.

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Section: Resultsmentioning

confidence: 99%

Neural Network Based Approach to Diagnose and Classify Monkeypox Disease

Sakshi,

Lodhi,

Kukreja

2023

IJCDS

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“…Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. These neural networks are designed to mimic the structure and function of the human brain, allowing them to identify more complex patterns and make decisions based on the data they have been trained with [ 10 , 11 ]. Deep learning has revolutionized the field of artificial intelligence, enabling machines to perform tasks that were once thought to be impossible.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Regenerative Medicine: Applications and Implications

Nosrati,

Nosrati

2023

Biomimetics

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The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field.

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