Automatic MRI Breast tumor Detection using Discrete Wavelet Transform and Support Vector Machines

Ibraheem, Amira Mofreh; Rahouma, Kamel H.; Hamed, Hesham F. A.

doi:10.1109/niles.2019.8909345

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…The confusion matrix's parameters are essential to computing the Sensitivity, Specificity, and Accuracy. The mathematical equation (3) shows the accuracy [24,25].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Deep Neural Network for Breast Tumor Classification Through Histopathological Image

Ibraheem

Rahouma

Hamed

2022

Journal of Advanced Engineering Trends

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Most oncologists differ in their opinions about the diagnosis using histopathological images, so the manual diagnosis of breast cancer is difficult, and it also needs high experience. Building a model of breast tumor classification is an essential task. Computer-aided diagnosis (CAD) enables efficient and accurate diagnosis of this type of imaging. Moreover, it helps early diagnosis of breast tumors. Deep learning has begun to evolve in the last decades, so convolutional neural networks (CNN) are used to classify breast tumors. We introduced the automatic breast cancer classification methods to help doctors in examination and treatment, which can automatically classify the histopathological images as benign or malignant. This study's theoretical basis is developing a deep learning tumor classification system. It uses a deep learning approach, i.e., the proposed CNN Network consists of three stages. The first stage is preprocessing. The second stage is the feature extraction stage that takes input as augmented preprocessed images. This stage has 2 phases. The first phase consists of three parallel CNN branches with different filter sizes to extract different feature levels. We merged the three parallel branches in the second phase, which produced a feature fusion path that reduced the time complexity and enhanced the designed method's performance. The third stage classifies the fused features as benign or malignant. The BreakHis database is used and implemented and contains 7909 images of breast tumor tissue for 82 patients. The dataset was divided into 60%, 20%, 20% for training, validation, and testing. Several tests were performed to achieve the best diagnostic accuracy of 91.37 %. The results are compared to the results of different techniques in the literature. The introduced advantages of our method make it help in the shortest treatment time, especially in the case of the large-scale histopathological images of the breast cancer dataset where the average testing time per image for the proposed method was 0.08s.

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“…The confusion matrix's parameters are essential to computing the Sensitivity, Specificity, and Accuracy. The mathematical equation (3) shows the accuracy [24,25].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Deep Neural Network for Breast Tumor Classification Through Histopathological Image

Ibraheem

Rahouma

Hamed

2022

Journal of Advanced Engineering Trends

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“…MRI Images SeveralauthorsusesMRIimagesforcancerdetection. Forexample, theauthorsin [36]introducedatwo-dimensional median filter for the detection of breast cancer in MRI images. They extracted features using discrete wavelet transform (DWT) and subsequently reduced the number of features using principal component analysis (PCA).…”

Section: 1mentioning

confidence: 99%

The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Mohamed

Amer²,

Eldin

et al. 2022

Journal of Computing and Communication

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According to the World Health Organization, cancer is the second leading cause of mortality. Breast cancer is the most prevalent cancer diagnosed in women around the world. Breast cancer diagnostics range from mammograms to CT scans and ultrasounds, but a biopsy is the only way to know for sure if the suspicious cells detected in the breast are cancerous or not. This paper's main contribution is multi-fold. First, it proposes a deep learning approach to detect breast cancer from biopsy microscopy images. Deep convolution nets of various types are used. Second, the paper examines the effects of different data preprocessing techniques on the performance of deep learning models. Third, the paper introduces an ensemble method for aggregating the best models in order to improve performance. The experimental results revealed that Densenet169, Resnet50, and Resnet101 are the three best models achieving accuracy scores of 62%, 68%, and 85%, respectively. without data preprocessing. With the help of data augmentation and segmentation, the accuracy of these models increased by 20%, 17%, and 6%, respectively. Additionally, the ensemble learning technique improves the accuracy of the models even further. The results show that the best accuracy achieved is 92.5%.

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“…The integration of adversarial network and convolutional neural network (CNN) requires a large amount of medical data for training the developed model, which was extremely expensive. Ibraheem et al [15] combined two dimensional median filter and discrete wavelet transform for improving the quality of breast images and extracting the features. The extracted features were given to the support vector machine (SVM) for tumor and healthy region classification.…”

Section: Introductionmentioning

confidence: 99%

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Kumar Mamdy,

Petli

2024

IJECE

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Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.

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Automatic MRI Breast tumor Detection using Discrete Wavelet Transform and Support Vector Machines

Cited by 15 publications

References 19 publications

Deep Neural Network for Breast Tumor Classification Through Histopathological Image

Deep Neural Network for Breast Tumor Classification Through Histopathological Image

The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

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