10 citations · 10 across the 3 of their papers we have counts for
3 papers
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
Omid Nejati Manzari, Javad Mirzapour Kaleybar, Hooman Saadat +1
The accurate segmentation of medical images is critical for various healthcare applications. Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) li…
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer
Javad Mirzapour Kaleybar, Hooman Saadat, Hooman Khaloo
This paper introduces a groundbreaking classification model called the Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical images. The CETC model combines…
Efficient Vision Transformer for Accurate Traffic Sign Detection
Javad Mirzapour Kaleybar, Hooman Khaloo, Avaz Naghipour
This research paper addresses the challenges associated with traffic sign detection in self-driving vehicles and driver assistance systems. The development of reliable and highly a…