Dr. Asmaa A. Hekal
Computer Science and Information Systems, Egyptian Chinese University, Cairo, Al Qāhirah, Egypt
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Academic Career Length
6Years (2021-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications8 January 2024 in IEEE Access
Journal Article
Asmaa A. Hekal
Ahmed ElnakibHMHossam El-Din Moustafa
Hossam El-Din Moustafa
HAHanan M. Amer
Hanan M. Amer
This paper introduces a deep learning approach for breast cancer segmentation from ultrasound imaging using a Dual Decoder Attention ResUNet (DDA-AttResUNet). DDA-AttResUNet utilizes a Dual Decoder Attention structure to simultaneously focus on tumor segmentation while also capturing supplementary contextual information, leading to enhanced segmentation accuracy. An Attention mechanism is incorporated to enhance the representation of segmented regions by effectively combining information from multiple sources. The model’s performance is validated on a public challenging dataset of 780 Breast Ultrasound Images (BUSI), achieving a Dice similarity coefficient of 92.92±0.69%, Intersection over Union of 87.39 ± 1.10%, Sensitivity of 92.16 ± 0.92%, Precision of 93.90 ± 0.40%, and Accuracy of 98.82 ± 0.10%, using 10-fold cross-validation. These results, comparable to other leading methods, indicate that our DDA-AttResUNet can significantly advance breast tumor segmentation in BUS imaging, with implications for improved diagnosis and patient outcomes.
7 April 2021 in Signal, Image and Video Processing
Journal Article
Asmaa A. HekalAhmed ElnakibHMHossam El-Din Moustafa
Hossam El-Din Moustafa2 April 2022 in Evolutionary Intelligence
Journal Article
Asmaa A. HekalHMHossam El-Din Moustafa
Hossam El-Din MoustafaAhmed Elnakib28 December 2024 in Biomedical Signal Processing and Control
Journal Article
Asmaa A. HekalHAHanan M. Amer
Hanan M. Amer
HMHossam El-Din Moustafa
Hossam El-Din MoustafaAhmed Elnakib1 December 2024 in Nile Journal of Communication and Computer Science
Journal Article
Asmaa A. HekalHMHossam El-Din Moustafa
Hossam El-Din Moustafa
HAHanan M. Amer
Hanan M. Amer
This study introduces DAtt-ResUNet++, an advanced deep learning model specifically designed to enhance breast tumor segmentation in ultrasound images. The model integrates a Dual-Attention mechanism within the ResUNet++ framework, significantly improving its ability to focus on tumor regions while capturing relevant contextual information from surrounding tissue. By combining both spatial and channel-based attention, DAtt-ResUNet++ achieves higher segmentation precision. For evaluation, the model was rigorously tested on a public dataset of 780 breast ultrasound images, utilizing a robust 10-fold cross-validation approach. It achieved impressive results with a Dice similarity coefficient of 90.40 ± 0.88%, Intersection over Union (IoU) of 84.62 ± 1.12%, Sensitivity of 89.82 ± 0.75%, Precision of 93.44 ± 0.56%, and Accuracy of 98.73 ± 0.12%. These results position DAtt-ResUNet++ as a competitive tool against state-of-the-art methods, showcasing its potential to improve breast tumor segmentation in ultrasound imaging. Future research will explore further optimizations and validation on additional datasets.
10 August 2025 in International Journal of Telecommunications
Journal Article
GSGhada Abd El-Latif Shatat
Ghada Abd El-Latif ShatatHMHossam El-Din Moustafa
Hossam El-Din MoustafaMSMohamed Sabry Saraya
Mohamed Sabry SarayaAMAl-Saeed Ahmed Marzouk
Al-Saeed Ahmed MarzoukAsmaa A. HekalAsthma, a prevalent and complex chronic respiratory disease, imposes a growing burden on global healthcare systems. This survey investigates the evolving role of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), in enhancing early detection, personalized care, and real-time monitoring of asthma. Drawing on a structured synthetic dataset encompassing demographic, environmental, lifestyle, and clinical variables, the study applies rigorous data preprocessing and comparative model evaluation across multiple ML algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and XGBoost. Among these, the XGBoost classifier outperforms others, achieving 98% accuracy and an AUC of 0.94, demonstrating its robustness for structured health data. Additionally, the integration of wearable and real-time sensor data is identified as a critical future direction to further improve predictive performance and clinical applicability. This review highlights the potential of AI-driven approaches to revolutionize asthma care by enabling timely interventions, reducing hospitalizations, and supporting individualized management strategies.
About Me
Asmaa A. Hekal earned her Ph.D. in Electronics and Communication Engineering, with a specialization in computer vision and artificial intelligence. Her research focuses on applying AI and deep learning techniques to medical image analysis and intelligent systems, with major contributions in breast cancer classification, brain tumor segmentation and classification, fetal head circumference estimation, and pneumonia detection. She has also led advanced projects on pulmonary embolism segmentation in CT scans, real-time polyp segmentation in colonoscopy videos, and age-invariant face recognition. Additionally, she has developed AI-driven recommendation systems and image similarity search models, showcasing expertise in deep neural networks, machine learning, and computer vision. Beyond her research, Dr. Hekal is an engaging lecturer who mentors and inspires future engineers and AI researchers. She is actively involved in quality assurance processes, accreditation standards, and curriculum development to ensure the continuous improvement of academic programs. In addition to teaching, she serves as a peer reviewer for leading journals and is committed to advancing innovative, data-driven solutions that bridge engineering, AI, and emerging technologies.