Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight m...
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Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. ...
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Scene Text Image Super-Resolution (STISR) has become a significant pre-processing technique to enhance text recognition accuracy in low-resolution scene text images, particularly when addressing the challenges of low-...
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Recent Face Super-resolution (FSR) based on iterative collaboration between facial image recovery network and landmark estimation has succeeded in super-resolving facial images. However, the existing noise in coarse f...
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Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assi...
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The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, es...
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Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight m...
Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight methodologies, although explored extensively, have fallen short of achieving the accuracy benchmarks attained by the model. The approach underwent rigorous testing on a challenging 2D T1-weighted CE-MRI dataset housing three distinct types of brain tumors: Meningioma, Glioma, and Pituitary. Recognizing the susceptibility of models to overfitting during training, introduced was a normalized combined channel and spatial attention mechanism, strategically incorporated to serve as an effective regularizer. Comparative analysis against state-of-the-art methodologies on this dataset highlights a notable performance boost of 2.12 percentage points achieved by integrating this attention mechanism into a base network. Furthermore, the fusion of the model with the pre-trained VGG16 network and GoogleNet into an ensemble demonstrates superior accuracy; however, this amalgamation comes at the expense of execution speed.
Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily b...
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Image compression is a topic of significant interest as it reduces file sizes in stored data. In this paper, we propose a model that achieves multiple levels of compression, thereby minimizing the storage space requir...
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Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients...
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