Journal of Oncology and Cancer Research

Abdullah Al Sakib is a cybersecurity specialist and multidisciplinary researcher with expertise in artificial intelligence, medical imaging, and cancer diagnostics. He is currently pursuing a Master’s degree in Information Technology with a specialization in cybersecurity at Westcliff University, California, and holds a B.Sc. in Computer Science and Engineering from American International University-Bangladesh.His research focuses on deep learning, transfer learning, and explainable AI, particularly in the areas of leukemia, lung cancer, and ovarian cancer diagnosis. With over 20 peer-reviewed publications and close to 200 academic citations, Mr. Sakib’s contributions have been recognized through several awards, including Best Paper honors at international conferences.

• General Medicine
• Medical Imaging
• Cancer Diagnostics

1.  Publications: 20+ peer-reviewed articles in deep learning, transfer learning, medical imaging, and cancer diagnostics

Professional Biography

2.  Citations: ~192 academic citations

Resume Of Abdullah Al Sakib -2-2

3.  Editorial Work: Editorial Board Member of the Journal of Oncology and Cancer Research

Professional Biography

4.  Peer Review: Experience as reviewer and collaborator in academic journals

Professional Biography

1.  Public Service Institutions Leading the Way with Innovative Clean Energy Solutions – 2022

2.  Advancing early leukemia diagnostics: A comprehensive study incorporating image processing and transfer learning – 2024

3.  Parasitology unveiled: Revolutionizing microorganism classification through deep learning – 2024

4.  Classroom activity classification with deep learning – 2024

5.  Discoverable Hidden Patterns in Water Quality through AI, LLMs, and Transparent Remote Sensing – 2024

6.  Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning – 2024

7.  A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis – 2024

8.  Advancements in Jute Leaf Disease Detection: A Comprehensive Study Utilizing Machine Learning and Deep Learning Techniques – 2024

9.  Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models – 2024

10. Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning – 2025

11. Robust feature selection for improved sleep stage classification – 2025

12. Hybrid deep learning for interpretable lung cancer recognition across computed tomography and histopathological imaging modalities – 2025

13. LEVit-Skin: A balanced and interpretable transformer-CNN model for multi-class skin cancer diagnosis – 2025

14. ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition – 2025

15. Dual-branch CrossViT for ovarian cancer diagnosis: Integrating and explainable AI for real-time clinical applications – 2025

16. Accelerated and Accurate Cervical Cancer Diagnosis Using a Novel Stacking Ensemble Method with Explainable AI – 2025

17. Eggplant Disease Diagnosis Using a Robust Ensemble of Transfer Learning Architectures – 2025

18. Automated Weed Species Classification in Rice Cultivation Using Deep Learning – 2025

19. Federated Learning-Based Robust Breast Cancer Recognition: Ensuring Privacy and Accuracy Across Distributed Data – 2025