Identifying Microscopic Augmented Images using Pre-Trained Deep Convolutional Neural Networks

Tiwari, Raj Gaurang; Misra, Alok; Khullar, Vikas; Agarwal, Ambuj Kumar; Gupta, Shubhi; Srivastava, Arun Pratap

doi:10.1109/ictai53825.2021.9673472

Cited by 15 publications

(2 citation statements)

References 26 publications

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“…Several algorithms enable deep learning, or deep structured learning [17]. Deep learning, like NN, contains a cascade of levels.…”

Section: Deep Learning In Medical Imagingmentioning

confidence: 99%

Skin cancer diagnosis using the deep learning advancements: a technical review

Pandey,

Shankhdhar

2024

Bulletin EEI

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It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.

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“…Several algorithms enable deep learning, or deep structured learning [17]. Deep learning, like NN, contains a cascade of levels.…”

Section: Deep Learning In Medical Imagingmentioning

confidence: 99%

Skin cancer diagnosis using the deep learning advancements: a technical review

Pandey,

Shankhdhar

2024

Bulletin EEI

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“…A strong deep learning architecture, deep convolutional neural networks (DCNNs) have been extensively used in many practical applications. Furthermore, it is extensively used in the field of medical imaging analysis because to its excellent performance in the field of computer vision and image analysis such as object identification, semantic segmentation and recognition [44], [45].…”

Section: Deep Learning Applied To Covid-19mentioning

confidence: 99%

Exploiting artificial intelligence for combating COVID-19: a review and appraisal

Sharma

Pandey²,

Agarwal³

2023

Bulletin EEI

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Machine learning algorithms immediately became critical in the battle against the COVID-19 outbreak. Diagnoses, medicine research, an illness spread predictions, and population surveillance all required the use of artificial intelligence (AI) methods as the epidemic grew in scope. To combat COVID-19, screening procedures that are both effective and rapid are required. At COVID-19, AI developers took a chance to show how AI can benefit all mankind. It was only after the employment of AI in the battle against COVID-19. AI's various and diverse applications in the epidemic are documented in this study. It is the purpose of this study to help shape the future development and usage of these technologies, whether in the present or future health crises.

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