Comparison Analysis of Deep Learning Models In Medical Image Segmentation

Tripathi, Shivangi; Wadhwani, Rajesh; Rasool, Akhtar; Jadhav, Abhishek

doi:10.1109/confluence56041.2023.10048822

Cited by 1 publication

(3 citation statements)

References 9 publications

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“…In this case, P stands for positive samples, while N stands for negative samples. Classifier's precision can be viewed as a gauge of its accuracy as in equation (25). Another sign of high false positive rates is a low precision.…”

Section: Results Analysismentioning

confidence: 99%

“…𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 (25) According to equation (26), recall determines which percentage of positively detected instances is the positive out of all positives. Recall can be viewed as a metric for gauging how thorough a classifier is.…”

Section: Results Analysismentioning

confidence: 99%

“…It provides an effective replacement when working with image collections by facilitating the production of synthetic samples. By creating novel instances and placing them strategically in the feature space for existing samples, this method aims to achieve equilibrium in the distribution of classes [25], [26], [27]. Following SMOTE, Table 4 displays chest radiographs taken for training.…”

Section: Data Balancing Smote (Synthetic Minority Oversampling Techni...mentioning

confidence: 99%

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An Ensemble Novel Deep Learning Technique for Chest Radiograph-Based Pneumonia Prediction

2023

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Pneumonia continues to be a prominent treatable cause of global mortality, stressing the importance of early identification to enable prompt intervention. Chest X-rays (CXRs) are an essential diagnostic tool, however determining their exact interpretation is still very difficult. By addressing both medical experts and individuals who are new to the area, the proposed work aims to improve prediction of pneumonia. The Synthetic Minority Over-sampling Technique has been utilised to cope with imbalanced dataset because the used dataset does not have balanced distribution among all classes. A pneumonia prediction model that makes use of convolutional neural networks including CustomVGG19, CustomResNet-50 and CustomDenseNet121 and the proposed ensemble model to improve diagnosis of pneumonia has been proposed. These models are trained and improved in experiments. The optimization of each model's performance was achieved through the systematic exploration of diverse configurations and hyperparameters. The ultimate outcomes were derived by employing the ensemble technique, which involved amalgamating the predictions of CNN models during the analysis. Results demonstrate the superiority of the proposed model, which achieved a 97.68% prediction accuracy.

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Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%