Hardware Accelerator for Real-Time Holographic Projector

Fadhel, Mohammed A.; Al-Shamma, Omran; Alzubaidi, Laith

doi:10.1007/978-3-030-49342-4_13

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These fully connected layers are used as the final network layer, and are concerned within the classification processes, as shown in Figure 1. CNNs are heavy task and required high computational tools to handle them such as GPUs [31][32][33][34] and FPGA [23,[35][36][37].…”

Section: Fully-connected Layersmentioning

confidence: 99%

Automated brain tumor classification using various deep learning models: a comparative study

Abbood

Shallal

Fadhel

2021

IJEECS

View full text Add to dashboard Cite

The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitations (i.e., precise quantitative measurements). Therefore, an automated classification system is necessary to avoid human mortality. The automatic categorization of brain tumors in the surrounding tumor region is a challenging task concerning space and structural variability. Four deep learning models: AlexNet, VGG16, GoogleNet, and RestNet50, are used in this comparative study to classify brain tumors. Based on accuracy, the results showed that RestNet50 is the best model with an accuracy of 95.8%, while AlexNet has the fast performance with a processing time of 1.2 seconds. In addition, a hardware parallel processing unit (GPU) is employed for real-time purposes, where AlexNet (the fastest model) has a processing time of only 8.3 msec.

show abstract