Breast Cancer Classification Using Deep Neural Networks

Karthik, S.; Perumal, R.; Mouli, P. V. S. S. R. Chandra

doi:10.1007/978-981-10-6680-1_12

Cited by 63 publications

(26 citation statements)

References 17 publications

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“…They showed that SVM-RFE achieved superior performance than several comparison methods. In addition, previous studies demonstrated DNN accuracy improvement by integrating RFE as the feature selection algorithm [38,39]. The experimental results showed that integration of SVM-RFE to DNN algorithms achieved best prediction accuracy as compared to other methods.…”

Section: Recursive Feature Eliminationmentioning

confidence: 87%

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

et al. 2020

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Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

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Section: Recursive Feature Eliminationmentioning

confidence: 87%

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

et al. 2020

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“…Several studies have been carried out, emphasizing the high performance of deep models in a wide variety of medical imaging applications, such as, for instance, detection of diabetic retinopathy [18], identification of arrhythmia [19], early prognostication of Alzheimer's disease dementia [20], diagnosis of brain hemorrhage [21], and classification of various types of cancer (e.g. breast [22], skin [23], brain [24], and prostate [25]).…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

Katsamenis

Protopapadakis

Voulodimos

et al. 2020

Preprint

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We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

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“…from the samples given to test the model. Accuracy, precision, sensitivity, recall, specificity and f-measure were calculated for proposed model to evaluate its performance [41].…”

Section: Model Evaluation and Performance Evaluation Metricsmentioning

confidence: 99%

Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA

Mohanraj¹,

Anbu²

2019

IJEAT

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Network Security is an important aspectin communication-related activities. In recent times, the advent of more sophisticated technologies changed the way the information is being sharedwith everyone in any part of the world.Concurrently, these advancements are mishandled to compromise the end-user devices intentionally to steal their personal information. The number of attacks made on targeted devices is increasing over time. Even though the security mechanisms used to defend the network is enhanced and kept updated periodically, new advanced methods are developed by the intruders to penetrate the system. In order to avoid these discrepancies, effective strategies must be applied to enhance the security measures in the network. In this paper, a machine learning-based approach is proposed to identify the pattern of different categories of attacks made in the past. KDD cup 1999 dataset is accessed to develop this predictive model. Bat optimization algorithm identifies the optimal parameter subset. Supervised machine learning algorithms were employed to train the model from the data to make predictions. The performance of the system is evaluated through evaluation metrics like accuracy, precision and so on. Four classification algorithms were used out of which, gradient boosting model outperformed the benchmarked algorithms and proved its importance on data classification based on the accuracy obtained from this model.

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Breast Cancer Classification Using Deep Neural Networks

Cited by 63 publications

References 17 publications

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA

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