Examinando por Autor "Elmaghraby, Adel Said"
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Ítem Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning(2025-01) Ebadi Jalal, Mona; Emam, Omar S.; Castillo Olea, Cristian; García-Zapirain, Begoña; Elmaghraby, Adel SaidNailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes associated with a wide range of systemic diseases, such as diabetes, cardiovascular disorders, and rheumatological conditions like systemic sclerosis, can manifest as observable microvascular changes in the terminal capillaries of nailfolds. The current gold standard relies on experts performing manual evaluations, which is an exhaustive time-intensive, and subjective process. In this study, we demonstrate the viability of a deep learning approach as an automated clinical screening tool. Our dataset consists of NFC images from a total of 225 participants, with normal images accounting for 6% of the dataset. This study introduces a robust framework utilizing cascade transfer learning based on EfficientNet-B0 to differentiate between normal and abnormal cases within NFC images. The results demonstrate that pre-trained EfficientNet-B0 on the ImageNet dataset, followed by transfer learning from domain-specific classes, significantly enhances the classifier's performance in distinguishing between Normal and Abnormal classes. Our proposed model achieved superior performance, with accuracy, precision, recall, F1 score, and ROC_AUC of 1.00, significantly outperforming both models of single transfer learning on the pre-trained EfficientNet-B0 and cascade transfer learning on a convolutional neural network, which each attained an accuracy, precision, recall, and F1 score of 0.67 and a ROC_AUC of 0.83. The framework demonstrates the potential to facilitate early preventive measures and timely interventions that aim to improve healthcare delivery and patients' quality of life.Ítem Breast lesions detection and classification via YOLO-based fusion models(Tech Science Press, 2021-06-04) Baccouche, Asma ; García-Zapirain, Begoña ; Castillo Olea, Cristian ; Elmaghraby, Adel SaidWith recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification. We evaluated the model on two publicly available datasets, with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and 235 mammograms from INbreast database. We also used a privately collected dataset with 487 mammograms. Furthermore, we suggested a fusion models approach to report more precise detection and accurate classification. Our best results reached a detection accuracy rate of 95.7%, 98.1% and 98% for mass lesions and 74.4%, 71.8% and 73.2% for calcification lesions, respectively on CBIS-DDSM, INbreast and the private dataset.Ítem Connected-UNets: a deep learning architecture for breast mass segmentation(Nature Research, 2021-12) Baccouche, Asma; García-Zapirain, Begoña; Castillo Olea, Cristian; Elmaghraby, Adel SaidBreast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.Ítem Diabetes type 2: poincaré data preprocessing for quantum machine learning(Tech Science Press, 2021-02-05) Sierra-Sosa, Daniel ; Arcila-Moreno, Juan D.; García-Zapirain, Begoña; Elmaghraby, Adel SaidQuantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising.Albeit being largely studied, VQC implementations for "real-world" datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By including feature selection techniques and geometrical transformations, enhanced quantum state preparation is achieved.Also, a representation based on the Stokes parameters in the Poincare Sphere is possible for visualizing the data.Our results showthat by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients. We used the implemented version of VQC available on IBM s framework Qiskit, and obtained with two and three qubits an accuracy of 70% and 72% respectively.Ítem Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques(Elsevier Ireland Ltd, 2022-06) Baccouche, Asma; García-Zapirain, Begoña; Zheng, Yufeng; Elmaghraby, Adel SaidBackground and Objective: Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy. Methods: We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix. Results: Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams. Conclusions: Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.Ítem Exploiting deep learning techniques for colon polyp segmentation(Tech Science Press, 2021-02-05) Sierra-Sosa, Daniel ; Patiño Barrientos, Sebastián; García-Zapirain, Begoña ; Castillo Olea, Cristian; Elmaghraby, Adel SaidAs colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC). These were trained and tested using CVC-Colon database, ETIS-LARIB Polyp, and a proprietary dataset. Three experiments were conducted to assess the techniques performance: 1) Training and testing using each database independently, 2) Mergingd the databases and testing on each database independently using a merged test set, and 3) Training on each dataset and testing on the merged test set. In our experiments, PANet architecture has the best performance in Polyp detection, and HTC was the most accurate to segment them. This approach allows us to employ Deep Learning techniques to assist healthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-Automated polyp detection in colonoscopies.Ítem An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks(Springer Nature, 2022-07-18) Baccouche, Asma; García-Zapirain, Begoña; Elmaghraby, Adel SaidA computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.Ítem Pressure injury image analysis with machine learning techniques: a systematic review on previous and possible future methods(Elsevier B.V., 2020-01) Zahia, Sofia; García-Zapirain, Begoña; Sevillano, Xavier; González, Alejandro; Kim, Paul J.; Elmaghraby, Adel SaidPressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.Ítem Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUS(IEEE Computer Society, 2019-10) Sierra-Sosa, Daniel; García-Zapirain, Begoña; Castillo Olea, Cristian; Oleagordia Ruiz, Ibon; Nuño Solinís, Roberto; Urtaran Laresgoiti, Maider; Elmaghraby, Adel SaidThe large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.