Deep Neural Network for Image Recognition In Medical Diagnosis

Authors

  • Siva Satya Sreedhar P , Ashok Reddy Kandula , K.Tamilarasi , Srikrishna Maan , Deema mohammed alsekait

DOI:

https://doi.org/10.47750/pnr.2022.13.S09.47

Abstract

Classification of medical images is a crucial aspect of clinical therapy and education. However, the performance of the conventional approach has reached its limit. In addition, the extraction and selection of classification features requires a substantial amount of time and effort when they are employed. Deep neural networks are an up-and-coming machine learning technique that has demonstrated their viability for various classification tasks. On a variety of picture classification tasks, the convolutional neural network notably achieves the best results. However, medical picture databases are difficult to obtain since they require a great deal of labelling skill. The medical consideration area is incredibly amazing compared to other industries. Despite the cost, there is a serious need in the area, and people expect a critical level of care. This paper describes the development of fictitious neural networks and a thorough analysis of DLA, both of which indicate promising clinical imaging applications. The majority of DLA executions focus on X-bar images, computer tomography images, mammography images, and advanced histopathology images. It provides a purposeful summary of the articles for the representation, identification, and division of clinical images taking DLA into consideration. This audit directs the analysts to consider proper changes in clinical picture examination in view of DLA.

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Published

2022-11-09 — Updated on 2022-11-09

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How to Cite

Siva Satya Sreedhar P , Ashok Reddy Kandula , K.Tamilarasi , Srikrishna Maan , Deema mohammed alsekait. (2022). Deep Neural Network for Image Recognition In Medical Diagnosis. Journal of Pharmaceutical Negative Results, 386–398. https://doi.org/10.47750/pnr.2022.13.S09.47

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Articles