What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Kenji Suzuki focuses on Artificial neural network, Artificial intelligence, Radiology, Computer-aided diagnosis and Medical imaging.
The Artificial neural network study combines topics in areas such as Feature extraction, Deep learning and Convolutional neural network.
His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Pattern recognition.
His Radiology study combines topics in areas such as Image processing, Cancer, Computer-aided and Nuclear medicine.
His Computer-aided diagnosis research is multidisciplinary, incorporating perspectives in Image segmentation and Radiography.
The various areas that he examines in his Medical imaging study include Segmentation and Computed tomography.
His most cited work include:
- Linear-time connected-component labeling based on sequential local operations (367 citations)
- Overview of deep learning in medical imaging (262 citations)
- Overview of deep learning in medical imaging (262 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Radiology.
His research on Artificial intelligence often connects related topics like Machine learning.
His biological study spans a wide range of topics, including Deep learning, Convolutional neural network and Nodule.
Kenji Suzuki interconnects False positive paradox, Mammography and Reduction in the investigation of issues within Pattern recognition.
His Radiology research includes elements of Computer aided detection and Lung.
His research in Image intersects with topics in Algorithm and Connected component.
He most often published in these fields:
- Artificial intelligence (53.76%)
- Computer vision (24.81%)
- Artificial neural network (25.94%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (53.76%)
- Pattern recognition (23.31%)
- Deep learning (6.02%)
In recent papers he was focusing on the following fields of study:
Kenji Suzuki mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Artificial neural network and Intermetallic.
The Artificial intelligence study combines topics in areas such as Machine learning and Reduction.
As a part of the same scientific family, he mostly works in the field of Pattern recognition, focusing on Filter and, on occasion, Sensitivity, Transfer of learning, Thoracic computed tomography, Region growing and Binary image.
His Artificial neural network research incorporates themes from Segmentation, Convolution and Ground-glass opacity.
His Convolutional neural network research integrates issues from False positive paradox, Training set and Feature.
The study incorporates disciplines such as Computer-aided diagnosis and Decision support system in addition to Medical imaging.
Between 2015 and 2021, his most popular works were:
- Overview of deep learning in medical imaging (262 citations)
- Overview of deep learning in medical imaging (262 citations)
- Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Kenji Suzuki focuses on Artificial intelligence, Deep learning, Convolutional neural network, Artificial neural network and Feature.
His Artificial intelligence research is multidisciplinary, relying on both Data mining, Reduction, Computer vision, Condition monitoring and Pattern recognition.
His Computer vision study incorporates themes from Outlier and Generalized Procrustes analysis.
Kenji Suzuki has researched Pattern recognition in several fields, including Selection, Binary number, Heuristic and Heuristic.
His Deep learning research includes themes of Probability distribution, Recurrent neural network, Imaging phantom, Nuclear medicine and Entropy.
His Artificial neural network research is included under the broader classification of Machine learning.
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