Adeel Abbasi

619 total citations
16 papers, 395 citations indexed

About

Adeel Abbasi is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Adeel Abbasi has authored 16 papers receiving a total of 395 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Cardiology and Cardiovascular Medicine and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Adeel Abbasi's work include AI in cancer detection (4 papers), Advanced Neural Network Applications (3 papers) and ECG Monitoring and Analysis (2 papers). Adeel Abbasi is often cited by papers focused on AI in cancer detection (4 papers), Advanced Neural Network Applications (3 papers) and ECG Monitoring and Analysis (2 papers). Adeel Abbasi collaborates with scholars based in Pakistan, United States and China. Adeel Abbasi's co-authors include Lal Hussain, Imtiaz Ahmed Awan, Kashif Javed Lone, Malik Sajjad Ahmed Nadeem, Timothy Q. Duong, Abdul Majid, Usman Tariq, Ghazanfar Farooq Siddiqui, Amjad Rehman and Tanzila Saba and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

Adeel Abbasi

15 papers receiving 374 citations

Peers

Adeel Abbasi

RNMI

CVPR

PRM

Deevyankar Agarwal Spain

Hieu H. Pham Vietnam

Haseeb Hassan China

Suane Pires P. da Silva Brazil

Rayan Krishnan United States

Arnab Kumar Mishra India

Mustafa Elattar Egypt

Yeşim Eroğlu Türkiye

Selene Tomassini Italy

Deevyankar Agarwal Spain

Adeel Abbasi

182 ×0.9

185 ×1.1

RNMI

81 ×0.8

CVPR

38 ×0.5

PRM

28 ×0.4

Citations per year, relative to Adeel Abbasi Adeel Abbasi (= 1×) peers Deevyankar Agarwal

Countries citing papers authored by Adeel Abbasi

Since Specialization

Citations

This map shows the geographic impact of Adeel Abbasi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adeel Abbasi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adeel Abbasi more than expected).

Fields of papers citing papers by Adeel Abbasi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adeel Abbasi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adeel Abbasi. The network helps show where Adeel Abbasi may publish in the future.

Co-authorship network of co-authors of Adeel Abbasi

This figure shows the co-authorship network connecting the top 25 collaborators of Adeel Abbasi. A scholar is included among the top collaborators of Adeel Abbasi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Adeel Abbasi. Adeel Abbasi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Chen, Baifan, et al.. (2025). MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation. AgriEngineering. 7(4). 103–103.

Deng, Xiaoheng, et al.. (2025). SC3D: Semantic-guided and Class-adaptive cross-domain fusion for 3D object detection in autonomous vehicles. Expert Systems with Applications. 268. 126359–126359. 1 indexed citations

Shah, Mohsin, Mushtaq Ali, Lal Hussain, et al.. (2025). A hybrid multi-panel image segmentation framework for improved medical image retrieval system. PLoS ONE. 20(2). e0315823–e0315823. 1 indexed citations

Abbasi, Adeel, C. Bermúdez, Daniel Brodie, et al.. (2023). Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery. Journal of Thoracic and Cardiovascular Surgery. 169(1). 114–123.e28. 5 indexed citations

Hussain, Lal, et al.. (2021). Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques. Mathematical Biosciences & Engineering. 18(3). 2882–2908. 6 indexed citations

Siddiqui, Ghazanfar Farooq, Amjad Rehman, Lal Hussain, et al.. (2021). Prostate Cancer Detection Using Deep Learning and Traditional Techniques. IEEE Access. 9. 27085–27100. 81 indexed citations

Hussain, Lal, Mushtaq Ali, Monagi H. Alkinani, et al.. (2021). Detecting brain tumors using deep learning convolutional neural network with transfer learning approach. International Journal of Imaging Systems and Technology. 32(1). 307–323. 39 indexed citations

Hussain, Lal, et al.. (2020). Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques. Waves in Random and Complex Media. 32(3). 1079–1102. 23 indexed citations

Abbasi, Adeel, et al.. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics. 14(4). 523–533. 97 indexed citations

10.

Hussain, Lal, Tony Nguyen, Haifang Li, et al.. (2020). Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. BioMedical Engineering OnLine. 19(1). 88–88. 83 indexed citations

11.

Riaz, Rabia, et al.. (2020). Deep learning scheme for character prediction with position-free touch screen-based Braille input method. Human-centric Computing and Information Sciences. 10(1). 19 indexed citations

12.

Hussain, Lal, Saima Rathore, Adeel Abbasi, & Sharjil Saeed. (2019). Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques. 134–134. 18 indexed citations

13.

Hussain, Lal, et al.. (2019). Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps. Technology and Health Care. 28(3). 259–273. 1 indexed citations

14.

Hussain, Lal, et al.. (2018). Arrhythmia detection by extracting hybrid features based on refined Fuzzy entropy (FuzEn) approach and employing machine learning techniques. Waves in Random and Complex Media. 30(4). 656–686. 17 indexed citations

15.

Hong, Kimberly N., Mark J. Russo, Mathew Williams, et al.. (2007). Patient-Specific Characteristics Determine Success of Surgical Atrial Fibrillation Ablation in Patients with Persistent Atrial Fibrillation. The Heart Surgery Forum. 10(6). E468–E472. 3 indexed citations

16.

Russo, Mark J., Timothy P. Martens, Darshan Doshi, et al.. (2006). Minimally Invasive Versus Standard Approach For Excision Of Atrial Masses. Innovations Technology and Techniques in Cardiothoracic and Vascular Surgery. 1(4). 207–207. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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