Blar i NTNU Open på forfatter "Alhaisoni, Majed"
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BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
Rehman, Shams ur; Khan, Muhamamd Attique; Masood, Anum; Almujally, Nouf Abdullah; Baili, Jamel; Alhaisoni, Majed; Tariq, Usman; Zhang, Yu-Dong (Journal article; Peer reviewed, 2023)The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have ... -
An Integrated Parallel Inner Deep Learning Models Information Fusion With Bayesian Optimization for Land Scene Classification in Satellite Images
Hamza, Ameer; Khan, Muhammad Attique; Ur Rehman, Shams; Albarakati, Hussain Mobarak; Alroobaea, Roobaea; Baqasah, Abdullah M.; Alhaisoni, Majed; Masood, Anum (Peer reviewed; Journal article, 2023)Classification of remote scenes in satellite imagery has many applications, such as surveillance, earth observation, etc. Classifying high-resolution remote sensing images in machine learning is a big challenge nowadays. ... -
MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection
Bibi, Sobia; Khan, Muhammad Attique; Shah, Jamal Hussain; Damaševičius, Robertas; Alasiry, Areej; Marzougui, Mehrez; Alhaisoni, Majed; Masood, Anum (Peer reviewed; Journal article, 2023)Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate ... -
SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm
Hussain, Muneezah; Khan, Muhammad Attique; Damaševičius, Robertas; Alasiry, Areej; Marzougui, Mehrez; Alhaisoni, Majed; Masood, Anum (Peer reviewed; Journal article, 2023)Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional ...