Deep Learning with EfficientNetB1 for detecting brain tumors in MRI images

Benkrama, Soumia; Hemdani, Nour El Houda

doi:10.1109/icaeccs56710.2023.10104761

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…EfficientNet has achieved state-of-the-art performance on various image classification benchmarks, including ImageNet, and is well-suited for deployment on limited resources, such as mobile devices and edge computing systems. EfficientNetB1 [48], a variation of EfficientNet, offers state-of-the-art performance while maintaining a small model size and low computational cost. It is suitable for applications on devices with RCEs due to its modest model size, manageable parameters, and relatively high accuracy.…”

Section: Efficientnetmentioning

confidence: 99%

“…Efficient and Fast; highly Accurate; versatile Architecture complex architecture EfficientNet demonstrates efficiency, speed, and accuracy, but it has complex architecture [34], [48] Each optimization technique has its own advantages and disadvantages. The appropriate optimization technique depends on the specific task requirements and trade-offs between different factors.…”

Section: Efficientnet (Efficientnetb0-efficientnetb7mentioning

confidence: 99%

“…While compound scaling can improve accuracy, it demands additional computational resources, memory, and CPU [35]. Accordingly, several DNN models such as MobileNet [32], [33], [36] [39], ResNet [18], [20], [23], [40] [44], InceptionNet [18], [40], [45], [46], DenseNet [29], [44], [47], and EfficientNet [34], [48] have incorporated these techniques for RCEbased image classification, each offering its own set of advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

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Deep neural networks optimization for resource-constrained environments: techniques and models

Careem,

Md Johar,

Khatibi

2024

IJEECS

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This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification.<p> </p>

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Section: Efficientnetmentioning

confidence: 99%

Section: Efficientnet (Efficientnetb0-efficientnetb7mentioning

confidence: 99%