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Hussien Khaled

Bio: Hussien Khaled is an academic researcher from Cairo University. The author has contributed to research in topics: Breast ultrasound & Breast cancer. The author has an hindex of 2, co-authored 3 publications receiving 126 citations.

Papers
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Journal ArticleDOI
TL;DR: The data presented in this article reviews the medical images of breast cancer using ultrasound scan using Breast Ultrasound Dataset, which is categorized into three classes: normal, benign, and malignant images.

501 citations

Journal ArticleDOI
TL;DR: An overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets is confirmed.
Abstract: Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagno-sis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women’s Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.

68 citations

Journal ArticleDOI
TL;DR: It is concluded that 6-month trastuzumab is a cost-effective option when compared to 1-year trastzumab in patients with HER2 +ve ABC in Egypt and provides health care decision makers with additional insights to best allocate available resources concurrently with the improvement of the Egyptian patient's outcomes.
Abstract: Introduction: Breast cancer is the most prevalent cancer among women in Egypt. Trastuzumab is administered with chemotherapy for patients with HER2-positive advanced breast cancer (HER2 + ve ABC) i...

3 citations


Cited by
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Journal ArticleDOI
TL;DR: The data presented in this article reviews the medical images of breast cancer using ultrasound scan using Breast Ultrasound Dataset, which is categorized into three classes: normal, benign, and malignant images.

501 citations

Journal ArticleDOI
TL;DR: Results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the predictions performance, and the tumor shape feature can improve the diagnostic effect.

156 citations

Proceedings ArticleDOI
TL;DR: MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets, and has compared several baseline methods, including open-source or commercial AutoML tools.
Abstract: We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at this https URL.

136 citations

Journal ArticleDOI
TL;DR: Experimental results based on a clinical dataset of 170 3D ABUS volumes collected from 107 patients indicate that the proposed multi-task framework improves tumor segmentation and classification over the single-task learning counterparts.

129 citations

Posted ContentDOI
TL;DR: A large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D, and benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
Abstract: We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 10,214 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at https://medmnist.com/.

121 citations