GoogleNet CNN Classifications for Diabetics Retinopathy

Mohammed, Aiman Hasan Mohammed; Cevik, Mesut

doi:10.1109/hora55278.2022.9799971

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The assessment criteria for the classification of rectal cancer lesions can measure the merit of the model. For the purpose of validating the model, we first trained the rectal cancer MRI image data separately using different conventional convolutional neural networks, including GoogleNet [17] , ResNet [18] , and VGG16 three network models, and the Accuracy of these models are shown in Table 3.…”

Section: Model Assessmentmentioning

confidence: 99%

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Wei,

Liu,

Wan

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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The new incidence rate of rectal cancer accounts for 12.2% of all cancers, which seriously affects the health of the people. In addition, the early clinical diagnosis of T staging of rectal cancer will strongly support the formulation of effective treatment plans. However, it is difficult for clinicians to accurately determine the T stage of rectal cancer before operation because the site of rectal tumor invasion is not obvious. Therefore, this paper first proposes a bilinear feature fusion mechanism, which effectively avoids the problem of information loss in the process of convolution neural network training of rectal cancer MRI images; Secondly, the new weighted loss function designed can solve the problem of multi-example imbalance; Finally, the experiment proves that the constructed intelligent prediction strategy for Tstage of rectal cancer has a good accuracy, which provides a good auxiliary result for the clinical treatment of rectal cancer.

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Section: Model Assessmentmentioning

confidence: 99%

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Wei,

Liu,

Wan

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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“…In order to accomplish this, five fine-tuned pre-trained CNN models have been used: GoogleNet [16], AlexNet [17], ShuffleNet [19], NASNet-Mobile [20], and SqueezeNet [18], respectively. These CNN models have been used in medical classification tasks and achieved good results, discussed in [22,23,25,[52][53][54]. Fine-tuned pre-trained models achieved superior or equal classification results compared to other, more complicated CNNs for COVID-19 classification [23,54].…”

mentioning

confidence: 99%

A robust MRI-based brain tumor classification via a hybrid deep learning technique

Nassar¹,

Yasser²,

Amer³

et al. 2023

J Supercomput

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The brain is the most vital component of the neurological system. Therefore, brain tumor classification is a very challenging task in the field of medical image analysis. There has been a qualitative leap in the field of artificial intelligence, deep learning, and their medical imaging applications in the last decade. The importance of this remarkable development has emerged in the field of biomedical engineering due to the sensitivity and seriousness of the issues related to it. The use of deep learning in the field of detecting and classifying tumors in general and brain tumors in particular using magnetic resonance imaging (MRI) is a crucial factor in the accuracy and speed of diagnosis. This is due to its great ability to deal with huge amounts of data and avoid errors resulting from human intervention. The aim of this research is to develop an efficient automated approach for classifying brain tumors to assist radiologists instead of consuming time looking at several images for a precise diagnosis. The proposed approach is based on 3064 T1-weighted contrast-enhanced brain MR images (T1W-CE MRI) from 233 patients. In this study, the proposed system is based on the results of five different models to use the combined potential of multiple models, trying to achieve promising results. The proposed system has led to a significant improvement in the results, with an overall accuracy of 99.31%.

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A New Technique using Deep Learning to Segment Retinal Blood Vessels

Ramya,

Jayaparvathy

2023

2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS)

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GoogleNet CNN Classifications for Diabetics Retinopathy

Cited by 5 publications

References 12 publications

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

A robust MRI-based brain tumor classification via a hybrid deep learning technique

A New Technique using Deep Learning to Segment Retinal Blood Vessels

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