Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture
2020287 citationsAhmet Çınar, Muhammed Yıldırımprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Ahmet Çınar'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 Ahmet Çınar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ahmet Çınar more than expected).
This network shows the impact of papers produced by Ahmet Çınar. 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 Ahmet Çınar. The network helps show where Ahmet Çınar may publish in the future.
Co-authorship network of co-authors of Ahmet Çınar
This figure shows the co-authorship network connecting the top 25 collaborators of Ahmet Çınar.
A scholar is included among the top collaborators of Ahmet Çınar 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 Ahmet Çınar. Ahmet Çınar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yıldırım, Muhammed & Ahmet Çınar. (2021). Classification of Skin Cancer Images with Convolutional Neural Network Architectures. DergiPark (Istanbul University).1 indexed citations
10.
Cengil, Emine & Ahmet Çınar. (2021). Poisonous Mushroom Detection using YOLOV5. DergiPark (Istanbul University). 16(1). 119–127.14 indexed citations
11.
Yıldırım, Muhammed & Ahmet Çınar. (2021). Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. DergiPark (Istanbul University). 16(1). 103–112.1 indexed citations
Cengil, Emine & Ahmet Çınar. (2017). Comparison of HOG (Histogram of Oriented Gradients) and Haar Cascade Algorithms with A Convolutional Neural Network Based Face Detection Approach. International journal of advance research, ideas and innovations in technology. 3(5). 244–255.2 indexed citations
Çınar, Ahmet, et al.. (1999). Detection of a new medium for budwood culture in vitro of citrus. TURKISH JOURNAL OF AGRICULTURE AND FORESTRY. 23. 333–340.6 indexed citations
16.
Çınar, Ahmet, et al.. (1999). Investigations on the Possibility to Obtain Mal Secco (Phoma tracheiphila Kanc. et Ghik.) Resistant Varieties Via Protoplast Fusion (Somatic Hybridization) in Lemon. TURKISH JOURNAL OF AGRICULTURE AND FORESTRY. 23. 157–168.2 indexed citations
17.
Özaslan, Mehmet & Ahmet Çınar. (1990). The detection of citrus exocortis viroid by polyacrylamide gel electrophoresis.. 19(2). 41–52.
18.
Çınar, Ahmet, et al.. (1990). The allelopathic effect of Raphanus sativus L.. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz. 259–264.2 indexed citations
19.
Tuzcu, Ö., et al.. (1989). Resistance of some Citrus species and hybrids to mal secco (Phoma tracheiphila Kanc. et Ghik.) disease.. Fruits. 44(3). 139–148.4 indexed citations
20.
Çınar, Ahmet, et al.. (1976). Resistance study of the citrus rootstocks to Phytophthora citrophthora (Smith & Smith) Leonian I. A research on the resistance of 14 different rootstocks to P. citrophthora (Smith & Smith) Leonian.. 5.
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.