Showrov Islam, M Aziz, Hadiur Rahman Nabil, Jamin Rahman Jim et al.
Generative Adversarial Networks are a class of artificial intelligence algorithms that consist of a generator and a discriminator trained simultaneously through adversarial training. GANs have found crucial applications in various fields, including medical imaging. In healthcare, GANs contribute by ...
Hadiur Rahman Nabil, Istyak Ahmed, Aritra Das, M. F. Mridha et al.
This study introduces MSFE-GallNet-X, a domain-adaptive deep learning model utilizing multi-scale feature extraction (MSFE) to improve the classification accuracy of gallbladder diseases from grayscale ultrasound images, while integrating explainable artificial intelligence (XAI) methods to enhance ...
Hashibul Ahsan Shoaib, Hadiur Rahman Nabil, Md Anisur Rahman, Md. Mohsin Kabir et al.
The rapid advancement of deep learning (DL) technologies has significantly transformed the domain of aerial image analysis. This systematic review explores the forefront of deep learning architectures specifically designed for the processing and analysis of aerial imagery. It offers a comprehensive ...
Mir Nafiul Nagib, Rahat Pervez, Afsana Alam Nova, Hadiur Rahman Nabil et al.
Brain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer bl...
MD Azam Khan, Arifur Rahman, Farhad Uddin Mahmud, Kanchon Kumar Bishnu et al.
Predictive maintenance is essential for ensuring the reliability and efficiency of wind energy systems. Traditional deep learning models for sensor fault detection rely solely on data-driven patterns, often lacking interpretability and robustness. This paper proposes a Physics-Guided Bayesian Neural...
Istyak Ahmed, Hadiur Rahman Nabil, Golam Rabbani Abir, Tazdik Hossain et al.
This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targe...
Sayed Sayem, Sayed Sumsul Islam Sanny, Hadiur Rahman Nabil
Hadiur Rahman Nabil, Md. Golam Rabbani Abir, Mst. Moushumi Khatun, Md. Eshmam Rayed et al.
Rufaida Mamun, Shalim Sadman, Hadiur Rahman Nabil, M. F. Mridha et al.
Hadiur Rahman Nabil, Fariya Sultana Prity, Mohammad Mahmudul Hasan, M.F. Mridha
The accurate and interpretable diagnosis of gastrointestinal (GI) conditions remains a significant clinical challenge, particularly across diverse populations such as adults and children. In this study, we propose a domain adaptive explainable Multi-Scale Convolutional Neural Network integrated with...
Forhan Bin Emdad, Mohammad Ishtiaque Rahman, Hadiur Rahman Nabil, Md. Eshmam Rayed et al.
Objectives: Alzheimer’s disease (AD) remains one of the most prevalent neurodegenerative conditions among older adults, underscoring the urgent need for accurate and ethically grounded early detection methods. Artificial intelligence (AI) techniques, particularly machine learning and deep learning m...
Aritra Das, Hadiur Rahman Nabil, Fahad Pathan, Momotaz Rahman Ouishy et al.
The assessment of fruit freshness is a critical factor in ensuring the quality and safety of food products, impacting both consumer satisfaction and market value. This paper presents a comparative evaluation of Vision Transformer (ViT) models for assessing fruit freshness using transfer learning tec...
Shahriar Siddique Ayon, Sharia Arfin Tanim, Hadiur Rahman Nabil, Maruful Islam et al.
Determining the health and resilience of ecosystems depends on an understanding of the dynamics of zooplankton abundance in tropical temporary ponds. Using ensemble modelling and explainable artificial intelligence (XAI) techniques, this paper explores the complex dynamics of zooplankton abundance i...