Brain Tumor Segmentation Based on an Optimized Convolutional Neural Network and an Improved Chimp Optimization Algorithm

Ranjbarzadeh, Ramin; Zarbakhsh, Payam; Caputo, Annalina; Tirkolaee, Erfan Babaee; Bendechache, Malika

doi:10.2139/ssrn.4295236

Cited by 2 publications

(1 citation statement)

References 50 publications

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“…Each layer mines higher-level features from the prior layer. Consequently, various DL models can obtain different solutions to explore the intrinsic high-level features that are beneficial for recommendation tasks [ 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

et al. 2023

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In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.

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Section: Introductionmentioning

confidence: 99%

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

et al. 2023

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MAU-Net: Mixed attention U-Net for MRI brain tumor segmentation

Zhang,

Han,

Zhang

2023

MBE

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<abstract><p>Computer-aided brain tumor segmentation using magnetic resonance imaging (MRI) is of great significance for the clinical diagnosis and treatment of patients. Recently, U-Net has received widespread attention as a milestone in automatic brain tumor segmentation. Following its merits and motivated by the success of the attention mechanism, this work proposed a novel mixed attention U-Net model, i.e., MAU-Net, which integrated the spatial-channel attention and self-attention into a single U-Net architecture for MRI brain tumor segmentation. Specifically, MAU-Net embeds Shuffle Attention using spatial-channel attention after each convolutional block in the encoder stage to enhance local details of brain tumor images. Meanwhile, considering the superior capability of self-attention in modeling long-distance dependencies, an enhanced Transformer module is introduced at the bottleneck to improve the interactive learning ability of global information of brain tumor images. MAU-Net achieves enhancing tumor, whole tumor and tumor core segmentation Dice values of 77.88/77.47, 90.15/90.00 and 81.09/81.63% on the brain tumor segmentation (BraTS) 2019/2020 validation datasets, and it outperforms the baseline by 1.15 and 0.93% on average, respectively. Besides, MAU-Net also demonstrates good competitiveness compared with representative methods.</p></abstract>

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Brain Tumor Segmentation Based on an Optimized Convolutional Neural Network and an Improved Chimp Optimization Algorithm

Cited by 2 publications

References 50 publications

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

MAU-Net: Mixed attention U-Net for MRI brain tumor segmentation

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