Motivation, Definition, Application and the Future of Edge Artificial Intelligence

Haldorai, Anandakumar; Anandakumar, Shrinand

doi:10.53759/181x/jcns202202011

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…In our study, we employed four different pre-trained transfer learning models [18][19][20] to identify breast cancer: ResNet-101, EfficientNet, VGG19, and ResNet-50.…”

Section: Transfer Driven Ensemble Based Classifiersmentioning

confidence: 99%

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

et al. 2023

JMC

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Cancer is a major cause of death that is brought on by the body's abnormal cell proliferation, including breast cancer. It poses a significant threat to the safety and health of people globally. Several imaging methods, such as mammography, CT scans, MRI, ultrasound, and biopsies, can help detect breast cancer. A biopsy is commonly done in histopathology to examine an image and assist in diagnosing breast cancer. However, accurately identifying the appropriate Region of Interest (ROI) remains challenging due to the complex nature of pre-processing phases, feature extracting regions, segmenting process and other conventional machine learning phases. This reduces the system's efficiency and accuracy. In order to reduce the variance that exists among viewers, the aim of this work is to build superior deep-learning phases algorithms. This research introduces a classifier that can detect and classify images simultaneously, without any human involvement. It employs a transfer-driven ensemble learning approach, where the framework comprises two main phases: production and detection of pseudo-color images and segmentation based on ROI Pooling CNN, which then feeds its output to ensemble models such as Efficientnet, ResNet101, and VGG19. Before the feature extraction process, data augmentation is necessary, involving minor adjustments like random cropping, horizontal flipping, and color space augmentations. Implementing and simulating the proposed segmentation and classification algorithms for any decision-making framework suggested could decrease the frequency of incorrect diagnoses and enhance classification accuracy. This could aid pathologists in obtaining a second opinion and facilitate the early identification of diseases. With a prediction accuracy of 98.3%, the proposed method outperforms the individual pre-trained models, namely Efficientnet, ResNet101, VGG16, and VGG19, by 2.3%, 1.71%, 2.01%, and 1.47%, respectively.

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“…In our study, we employed four different pre-trained transfer learning models [18][19][20] to identify breast cancer: ResNet-101, EfficientNet, VGG19, and ResNet-50.…”

Section: Transfer Driven Ensemble Based Classifiersmentioning

confidence: 99%

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

et al. 2023

JMC

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Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Endo

2023

JRS

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Representation learning or feature learning refers to a collection of methods employed in machine learning, which allows systems to autonomously determine representations needed for classifications or feature detection from unprocessed data. Representation learning algorithms are specifically crafted to acquire knowledge of conceptual features that define data. The field of state representation learning is centered on a specific type of representation learning that involves the acquisition of low-dimensional learned features that undergo temporal evolution and are subject to the influence of an agent's actions. Over the past few years, deep architecture have been widely employed for representation learning and have demonstrated exceptional performance in various tasks, including but not limited to object detection, speech recognition, and image classification. This article provides a comprehensive overview of the evolution of techniques for data representation learning. Our research focuses on the examination of conventional feature learning algorithms and advanced deep learning models. This paper presents an introduction to data representation learning history, along with a comprehensive list of available resources such as online courses, tutorials, and books. Additionally, various tool-boxes are also provided for further exploration in this field. In conclusion, this article presents remarks and future prospects for data representation learning.

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Motivation, Definition, Application and the Future of Edge Artificial Intelligence

Cited by 2 publications

References 23 publications

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

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