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[HTML][HTML] A hybrid deep learning model for brain tumour classification
A brain tumour is one of the major reasons for death in humans, and it is the tenth most
common type of tumour that affects people of all ages. However, if detected early, it is one of …
common type of tumour that affects people of all ages. However, if detected early, it is one of …
A novel approach for classifying brain tumours combining a squeezenet model with svm and fine-tuning
Cancer of the brain is most common in the elderly and young and can be fatal in both. Brain
tumours can heal better if they are diagnosed and treated quickly. When it comes to …
tumours can heal better if they are diagnosed and treated quickly. When it comes to …
YOLO-GW: quickly and accurately detecting pedestrians in a foggy traffic environment
X Liu, Y Lin - Sensors, 2023 - mdpi.com
In practice, the object detection algorithm is limited by a complex detection environment,
hardware costs, computing power, and chip running memory. The performance of the …
hardware costs, computing power, and chip running memory. The performance of the …
Rethinking weight decay for efficient neural network pruning
Introduced in the late 1980s for generalization purposes, pruning has now become a staple
for compressing deep neural networks. Despite many innovations in recent decades …
for compressing deep neural networks. Despite many innovations in recent decades …
[HTML][HTML] Deep convolutional neural network optimization for defect detection in fabric inspection
This research is aimed to detect defects on the surface of the fabric and deep learning model
optimization. Since defect detection cannot effectively solve the fabric with complex …
optimization. Since defect detection cannot effectively solve the fabric with complex …
Efficient federated learning for distributed neuroimaging data
Recent advancements in neuroimaging have led to greater data sharing among the
scientific community. However, institutions frequently maintain control over their data, citing …
scientific community. However, institutions frequently maintain control over their data, citing …
[HTML][HTML] Robust CNN compression framework for security-sensitive embedded systems
Convolutional neural networks (CNNs) have achieved tremendous success in solving
complex classification problems. Motivated by this success, there have been proposed …
complex classification problems. Motivated by this success, there have been proposed …
[HTML][HTML] Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN)
algorithms has emerged as a widely adopted technique, with particular attention on sparse …
algorithms has emerged as a widely adopted technique, with particular attention on sparse …