1 citations · 1 across the 5 of their papers we have counts for
7 papers
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis
Kasumi Obi, Quan Huu Cap, Noriko Umegaki-Arao +2
Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indi…
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
Quan Huu Cap, Hitoshi Iyatomi, Atsushi Fukuda
Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy…
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance.…
LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada +1
Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from o…
Super-Resolution for Practical Automated Plant Disease Diagnosis System
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of sy…
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis
Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada +2
In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision.…