Cloud storage based diagnosis of breast cancer using novel transfer learning with multi-layer perceptron

Jahangeer, Gul Shaira Banu; Rajkumar, T. Dhiliphan

doi:10.1007/s13198-021-01603-z

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…The multilayer perceptron technique was used in the study to build a prediction system with a hidden layer, an epoch, and a set number of neurons. Similar to this, a different study [29] divided regions afflicted by cancer and stored relevant information in the cloud using transfer learning-based cancer segmentation (TL-CAN-Seg) technology. A unique MLP and an altered Levenberg-Marquardt (LM) algorithm were used to interpret complex picture patterns and accomplish precise classification of areas affected by breast cancer, improving the accuracy of breast cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Networks in the Cloud

Awan

2023

IJAEMS

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Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Networks in the Cloud

Awan

2023

IJAEMS

View full text Add to dashboard Cite

show abstract

Cloud storage based diagnosis of breast cancer using novel transfer learning with multi-layer perceptron

Cited by 1 publication

References 31 publications

Deep Learning Neural Networks in the Cloud

Deep Learning Neural Networks in the Cloud

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