Design and Implementation of Machine Learning Evaluation Metrics on HPCC Systems

Suryanarayanan, A.; Chala, Arjuna; Xu, Lili; Shobha, G; Shetty, Jyoti; Dev, Roger

doi:10.1109/csitss47250.2019.9031056

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…Overall, it can be observed that our proposed DHS‐CapsNet can distinguish and subclassify histopathologic images with high precision and recall compared the 56 method.…”

Section: Discussionmentioning

confidence: 78%

“…Figure 6 shows a comparison of receiver operation characteristic curves of the traditional CapsNet (Figure 6A) and DHS-CapsNet (Figure 6B), which is a graphical plot to indicate the ability of the classifier to distinguish between classes. 56 AUC is one of the most widely used evaluation metrics. Specifically, the AUC is used as an evaluation metric for binomial classification.…”

Section: Discussionmentioning

confidence: 99%

“…Last, area under receiver operation characteristic curve was used for the model evaluation. Figure 6 shows a comparison of receiver operation characteristic curves of the traditional CapsNet (Figure 6A) and DHS‐CapsNet (Figure 6B), which is a graphical plot to indicate the ability of the classifier to distinguish between classes 56 . AUC is one of the most widely used evaluation metrics.…”

Section: Discussionmentioning

confidence: 99%

“…Table 3 shows results on our model compared with previous work 56 that used the LC25000 dataset as an input. The evaluation of both approaches is based on Recall and Precision performance metrics.…”

Section: Discussionmentioning

confidence: 99%

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DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Adu

Cai

et al. 2021

Int J Imaging Syst Tech

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This paper proposes a new dual horizontal squash capsule network (DHS-CapsNet) to classify the lung and colon cancers on histopathological images. DHS-CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2-lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS-CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS-CapsNet provides the top-1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer-aided diagnostic method to support doctors in lung and colon cancer diagnostics.

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“…Overall, it can be observed that our proposed DHS‐CapsNet can distinguish and subclassify histopathologic images with high precision and recall compared the 56 method.…”

Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Adu

Cai

et al. 2021

Int J Imaging Syst Tech

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“…Due to the cluster computing scalability of the platform, it provides for processing many petabytes of data [9], making it immensely powerful for its applications in machine learning [10]. For this, a bundle for performing machine learning operations on the ECL language was developed and is available as an open source bundle hosted on github [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Implementation of generative adversarial networks in HPCC systems using GNN bundle

Karthik¹,

Shetty²,

Shobha³

et al. 2021

IJ-AI

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<span id="docs-internal-guid-8df40bfb-7fff-fcb8-b52f-54f39570d649"><span>HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In order to make sure that they train as adversaries, a certain weights transfer methodology was implemented. MNIST dataset which has been used to test the proposed approach has provided satisfactory results. The results obtained were unique images very similar to the MNIST dataset, as it were expected.</span></span>

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A Personalized Cancer Diagnosis using Machine Learning Models Based on Big Data

Singh

Jain

2020

2020 Fourth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Design and Implementation of Machine Learning Evaluation Metrics on HPCC Systems

Cited by 4 publications

References 8 publications

DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Implementation of generative adversarial networks in HPCC systems using GNN bundle

A Personalized Cancer Diagnosis using Machine Learning Models Based on Big Data

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