Implementation of optimization algorithms on Wisconsin Breast cancer dataset using deep neural network

Darapureddy, Nagadevi; Karatapu, Nagaprakash; Battula, Tirumala Krishna

doi:10.1109/rteict46194.2019.9016822

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Nahid et al [ 20 ] proposed the DNN technique for breast cancer picture classification using convolutional neural networks (CNNs), long short-term memory (LSTM), and a combination of CNN and LSTM. Also, Darapurredy et al [ 21 ] used the deep neural network for classifying breast cancer.Feature-selection methods are used to reduce the number of features and selected the subset of features that improve the performance of classification algorithms. For example, Habib et al [ 22 ] applied the genetic programming (GP) as the feature-selection method to select the important features from the BCWD database.…”

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh

ghany²,

Alyami

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras–Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models.

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Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh

ghany²,

Alyami

et al. 2022

Computational Intelligence and Neuroscience

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“…In [6] the correctness of data classification with respect to accuracy, a model and various optimization algorithms are developed where they are feasible for computer aided diagnosis. Wisconsin breast cancer is a dataset with the features of digitized image.…”

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confidence: 99%

“…In [6] three different models are built such as two-layer sigmoid neurons, threelayer sigmoid neurons and four-layer sigmoid neurons. Sigmoid neuron induces a small change in the output with respect to changes in the input.…”

mentioning

confidence: 99%

“…SGD is linked with a random probability where some samples are selected for each iteration. In [6] 70% data is considered for training and 30% is considered for testing. Training accuracy, testing accuracy, training loss, testing loss for RMS propagation shows various changes but for SGD it approximately shows no change on this model with a sigmoid activation function to minimize the binary cross entropy loss.…”

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confidence: 99%

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Early Stage Detection and Classification of Breast Cancer

Reddy¹,

Mohan²,

Chandana³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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One of the major diseases that affect young to old aged women in recent times is breast cancer. It almost ranks as the first cause for death in women across the world. The survival rate of people suffering with it ranges somewhere between 40% and 60% depending on the development terms of particular countries. Hence, it becomes quite important to be able to diagnose such a disease at a stage as early as possible, so the patient could look out on the available options for treatment. Therefore, in this project, we propose such a breast cancer detection system which predicts the nature of the cancer, either benign or malignant by processing the mammographic image of the patient. The model basically uses a range of digital image processing techniques and also algorithms of ML in the process to output the prediction. It is trained using the MIAS breast cancer dataset. The input image is first resized, gray-scaled, and a gaussian filter is applied on it to remove background noises. It is then segmented and fed to the neural network, which gives the output prediction as an integer value (each value corresponding to a predicted class). The project also has a second stage where the severity of the cancer is also detected by taking input of other detailed attributes of the mammogram.

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Efficient Techniques for Detecting Malignant Tumor in Breast at an Early Stage: A Conceptual and Technological Review

Singh

Srivastava

2021

Advances in Mechanical Engineering

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Implementation of optimization algorithms on Wisconsin Breast cancer dataset using deep neural network

Cited by 7 publications

References 7 publications

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Early Stage Detection and Classification of Breast Cancer

Efficient Techniques for Detecting Malignant Tumor in Breast at an Early Stage: A Conceptual and Technological Review

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