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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion
Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, et al.
Microscopy Research and Technique (2019) Vol. 82, Iss. 6, pp. 741-763
Closed Access | Times Cited: 90

Showing 1-25 of 90 citing articles:

Brain tumor detection and classification using machine learning: a comprehensive survey
Javeria Amin, Muhammad Sharif, Anandakumar Haldorai, et al.
Complex & Intelligent Systems (2021) Vol. 8, Iss. 4, pp. 3161-3183
Open Access | Times Cited: 281
Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
Amjad Rehman, Muhammad Attique Khan, Tanzila Saba, et al.
Microscopy Research and Technique (2020) Vol. 84, Iss. 1, pp. 133-149
Closed Access | Times Cited: 249
Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges
Tanzila Saba
Journal of Infection and Public Health (2020) Vol. 13, Iss. 9, pp. 1274-1289
Open Access | Times Cited: 242
Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction
Tanzila Saba, Muhammad Attique Khan, Amjad Rehman, et al.
Journal of Medical Systems (2019) Vol. 43, Iss. 9
Closed Access | Times Cited: 223
Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework
Muhammad Attique Khan, Tallha Akram, Yudong Zhang, et al.
Pattern Recognition Letters (2021) Vol. 143, pp. 58-66
Closed Access | Times Cited: 210
Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification
Amjad Rehman, Siraj M. Khan, Majid Harouni, et al.
Microscopy Research and Technique (2021) Vol. 84, Iss. 7, pp. 1389-1399
Closed Access | Times Cited: 190
Brain tumor detection and multi‐classification using advanced deep learning techniques
Tariq Sadad, Amjad Rehman, Asim Munir, et al.
Microscopy Research and Technique (2021) Vol. 84, Iss. 6, pp. 1296-1308
Closed Access | Times Cited: 167
A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection
Muhammad Rashid, Muhammad Attique Khan, Majed Alhaisoni, et al.
Sustainability (2020) Vol. 12, Iss. 12, pp. 5037-5037
Open Access | Times Cited: 139
Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine
Farhat Afza, Muhammad Sharif, Muhammad Attique Khan, et al.
Sensors (2022) Vol. 22, Iss. 3, pp. 799-799
Open Access | Times Cited: 113
A survey, review, and future trends of skin lesion segmentation and classification
Md. Kamrul Hasan, Md. Asif Ahamad, Choon Hwai Yap, et al.
Computers in Biology and Medicine (2023) Vol. 155, pp. 106624-106624
Open Access | Times Cited: 86
A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features
Himanshu K. Gajera, Deepak Ranjan Nayak, Mukesh A. Zaveri
Biomedical Signal Processing and Control (2022) Vol. 79, pp. 104186-104186
Closed Access | Times Cited: 80
Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey
Mehwish Zafar, Muhammad Imran Sharif, Muhammad Irfan Sharif, et al.
Life (2023) Vol. 13, Iss. 1, pp. 146-146
Open Access | Times Cited: 49
LBO-MPAM: Ladybug Beetle Optimization-based multilayer perceptron attention module for segmenting the skin lesion and automatic localization
V. Sellam, Kannan Natrajan, Senthil Pandi S, et al.
Journal of Experimental & Theoretical Artificial Intelligence (2024), pp. 1-26
Closed Access | Times Cited: 26
Gastrointestinal diseases segmentation and classification based on duo-deep architectures
Mehshan Ahmed Khan, Muhammad Attique Khan, Fawad Ahmed, et al.
Pattern Recognition Letters (2019) Vol. 131, pp. 193-204
Closed Access | Times Cited: 140
Developed Newton-Raphson based deep features selection framework for skin lesion recognition
Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, et al.
Pattern Recognition Letters (2019) Vol. 129, pp. 293-303
Closed Access | Times Cited: 134
Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection
Muhammad Attique Khan, Saddaf Rubab, Asifa Kashif, et al.
Pattern Recognition Letters (2019) Vol. 129, pp. 77-85
Closed Access | Times Cited: 121
Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion
Alishba Adeel, Muhammad Attique Khan, Muhammad Sharif, et al.
Sustainable Computing Informatics and Systems (2019) Vol. 24, pp. 100349-100349
Closed Access | Times Cited: 115
Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation
Sajid Iqbal, Muhammad Usman Ghani Khan, Tanzila Saba, et al.
Microscopy Research and Technique (2019) Vol. 82, Iss. 8, pp. 1302-1315
Closed Access | Times Cited: 105
Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction
Amjad Rehman, Muhammad Attique Khan, Zahid Mehmood, et al.
Microscopy Research and Technique (2020) Vol. 83, Iss. 4, pp. 410-423
Closed Access | Times Cited: 93
A comparative study of features selection for skin lesion detection from dermoscopic images
Rabia Javed, Mohd Shafry Mohd Rahim, Tanzila Saba, et al.
Network Modeling Analysis in Health Informatics and Bioinformatics (2019) Vol. 9, Iss. 1
Closed Access | Times Cited: 86
A hierarchical three-step superpixels and deep learning framework for skin lesion classification
Farhat Afza, Muhammad Sharif, Mamta Mittal, et al.
Methods (2021) Vol. 202, pp. 88-102
Closed Access | Times Cited: 85
Entropy‐controlled deep features selection framework for grape leaf diseases recognition
Alishba Adeel, Muhammad Attique Khan, Tallha Akram, et al.
Expert Systems (2020) Vol. 39, Iss. 7
Closed Access | Times Cited: 78
Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features
Tanzila Saba
Microscopy Research and Technique (2021) Vol. 84, Iss. 6, pp. 1272-1283
Open Access | Times Cited: 74
Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review
Anuja Bhargava, Atul Bansal
Multimedia Tools and Applications (2021) Vol. 80, Iss. 13, pp. 19931-19946
Open Access | Times Cited: 58
Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review
Khalid M. Hosny, Doaa Elshoura, Ehab R. Mohamed, et al.
IEEE Access (2023) Vol. 11, pp. 85467-85488
Open Access | Times Cited: 27
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