Food image recognition using CNN

Srigurulekha, K; Ramachandran, Varun

doi:10.1109/iccci48352.2020.9104078

Cited by 12 publications

(1 citation statement)

References 8 publications

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“…In recent years, deep neural network (DNN)-based methods have achieved high performance for brain tumor segmentation (5)(6)(7)(8). Convolutional neural networks (CNNs) (9) have achieved great success in many research areas, such as image recognition (10)(11)(12), image segmentation (13)(14)(15), and natural language processing (16,17). In (18), a fully convolutional neural network (FCN) for image pixel-level image classification was proposed, which solves the problem of semantic-level image segmentation with input data of arbitrary size.…”

Section: Progress On Image Segmentation Methodsmentioning

confidence: 99%

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Wang

Luan

et al. 2022

Front. Oncol.

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Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

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