Detection of Lung Malignancy Using SqueezeNet-Fc Deep Learning Classification Technique

Kumar, Vinod; Bakariya, Brijesh

doi:10.1007/978-981-16-5747-4_59

Cited by 1 publication

(3 citation statements)

References 27 publications

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“…The results on challenging enlargement scale factor ×8 observed that more blurry results were generated by Bicubic, RFL [5], SelfExSR [62], SRCNN [6], and FSRCNN [7]. However, it is texture detail and effectively suppresses the artifacts, because our approach follow the concept of SqueezeNet [51] in which size is extremely compact for mobile applications and has only 1.2 million parameters but achieves an accuracy similar to AlexNet [64]. During the design of SqueezeNet, the architecture used 26 convolution neural network layers without a fully-connected layer.…”

Section: Quantitative Analysis Of Run Time Versus Psnrmentioning

confidence: 94%

“…During the design of SqueezeNet, the architecture used 26 convolution neural network layers without a fully-connected layer. SqueezeNet achieves a top-1 accuracy of 57.4% and a top-5 accuracy of 80.5% on ImageNet [64].…”

Section: Quantitative Analysis Of Run Time Versus Psnrmentioning

confidence: 99%

“…The potential applications of SqueezeNet techniques are various in the field of image and computer vision tasks. The main versatile application of SqueezeNet in healthcare [64] and self-driving cars [66], where compact efficient models are highly desirable. Self-driving cars rely heavily on real-time object detection to safely navigate through their environment.…”

Section: Quantitative Analysis Of Run Time Versus Psnrmentioning

confidence: 99%

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SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution

2023

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Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR). The primary target of SISR is to recover the visually appealing high-resolution (HR) output image from the original degraded low-resolution (LR) input image. However, most recent convolutional neural networks (CNNs)based image super-resolution frameworks often used a deeper and broader network architecture that requires a sizeable computational resource, risk of overfitting, increases computational complexity, and more memory consumption, as well as takes more processing time during the evaluations. To address these issues, we have presented a Squeeze-and-ExcitationNext for Single Image Super-Resolution approach, known as SENext. In brief, the squeeze-and-excitation blocks (SEB) are used in our network architecture with a view to reduce the computational cost and adopt the channel-wise feature mappings to recalibrate the features adaptively. Furthermore, local, sub-local, and global skip connections are employed between each SEB to enable the feature reusability and stabilize training convergence smoothly. Instead of hand-designed bicubic upsampling at pre-processing step, we have performed post-upsampling at the later stage to reconstruct the high-resolution image. Extensive quantitative and qualitative experiments are performed on the benchmark test dataset, including Set5, Set14, BSDS100, Urban100, and Manga109. These experimental evaluations validate the superiority of the SENext over other deep CNN image SR methods in terms of PSNR/SSIM, FLOPs, Number of parameters, processing speed, and visually pleasing effect.

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