Improved the detection and classification of breast cancer using hyper parameter tuning

Kousalya, K.; Saranya, T.

doi:10.1016/j.matpr.2021.03.707

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Kousalya et al [ 155 ] compared the self-made CNN model with DensenNet201 for the classification of breast cancer. In the self-made CNN model, there were two convolutional layers, two pooling layers, one flattened layer, and two fully connected layers.…”

Section: Application Of Cnn In Breast Cancermentioning

confidence: 99%

A Survey of Convolutional Neural Network in Breast Cancer

Zhu¹,

Wang²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

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Section: Application Of Cnn In Breast Cancermentioning

confidence: 99%

A Survey of Convolutional Neural Network in Breast Cancer

Zhu¹,

Wang²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…A DenseNet deep learning framework extracted image features and classifed cancerous and benign cells by feeding them into a fully connected (FC) layer. Te efectiveness of this technique was evaluated by adjusting the hyperparameters [50]. An algorithm named DICNN was developed by Irfan et al [51], which uses a dilated semantic segmentation network and morphological operation.…”

Section: Related Workmentioning

confidence: 99%

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Bukht

Ahmad²,

Almadhor

et al. 2023

Computational Intelligence and Neuroscience

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Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

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“…High performance is aimed at the machine learning concept by adjusting the hyperparameter values of the classi er algorithms. There are many studies [19][20][21][22][23][24][25] in the literature dealing with this issue. In this study, Randomize Search cross-validation and bayesian search cross-validation methods were used for the optimal combination of SVM hyperparameters.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Akyol

2023

Preprint

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Coronary artery disease outstands health problem that causes high mortality in the world population. This disease brings with it fateful problems such as heart attack and heart failure in patients with cardiovascular problems. Early diagnosis of coronary artery disease is essential for the timely administration of the right treatment and reduction of mortality. Angiography is the most preferred method for CAD detection. However, the complications and costs of this method have led researchers to forage alternative methods through machine learning algorithms. By developing a machine learning model with high generalization ability, prediction errors can be minimized. Thus, these models could potentially be useful for specialist physicians in the effective detection of coronary artery disease. The main focus of this study is to perform coronary artery disease detection with improved support vector machines. k-fold cross-validation experiments were performed on the Z-Alizadeh Sani dataset to evaluate the performance of the models. According to the results obtained, support vector machines with randomized search cross-validation provided the best performance when compared to other models. 87.102% average accuracy, 91.176% average sensitivity, 90.852% average precision, 76.996% average specificity, and also 8.824% average false negative rate obtained by 5-fold cross-validation competes with the known approaches in the literature.

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Improved the detection and classification of breast cancer using hyper parameter tuning

Cited by 12 publications

References 10 publications

A Survey of Convolutional Neural Network in Breast Cancer

A Survey of Convolutional Neural Network in Breast Cancer

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

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