MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network

Ranjbarzadeh, Ramin; Sarshar, Nima; Ghoushchi, Saeid Jafarzadeh; Esfahani, Mohammad Saleh; Parhizkar, Mahboub; Pourasad, Yaghoub; Anari, Shokofeh; Bendechache, Malika

doi:10.1007/s10479-022-04755-8

Cited by 40 publications

(27 citation statements)

References 74 publications

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“…In the context of data mining and analysis, deep learning models (DLs) are currently paving new avenues for POI recommendation systems. Unlike the earlier shallow neural network approaches, such as artificial neural networks (ANNs) that have been under exploration for many years, DL-based structures are characterized using a considerably amplified number of continuously linked neural layers [ 48 , 49 , 50 , 51 ]. This amplified number of layers is able to mine hidden patterns and higher-level features and can discover more difficult and hierarchical relationships.…”

Section: Methodsmentioning

confidence: 99%

“…We assess the accuracy and performance of the suggested model and the baseline techniques utilizing two common evaluation metrics, namely Recall at N (Recall@N), and Precision at N (Precision@N) [ 1 , 3 , 49 , 57 , 60 ]. These two scores are computed by matching each estimated location outcome with its corresponding true locations for achieving the correct order of top-K POIs for a user.…”

Section: Methodsmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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“…Recently, a large number of researchers take CNN as the backbone model for transfer learning, such as ResNet, AlexNet, DenseNet, and so on [ 85 – 87 ]. Some layers of CNN models are frozen, and the unfrozen layers are retrained with the data set [ 88 – 90 ]. Sometimes researchers use CNN models as feature extractors and select other networks as the classifiers [ 91 – 93 ], such as support vector machines (SVM) [ 94 ], randomized neural networks (RNNs) [ 95 ], etc.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Ranjbarzadeh et al [ 90 ] proposed a new CNN with multiple feature extraction paths for the segmentation of breast cancer (MRFE-CNN), as shown in Fig. 17 .…”

Section: Application Of Cnn In Breast Cancermentioning

confidence: 99%

A Survey of Convolutional Neural Network in Breast Cancer

Zhu¹,

Wang²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

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“…In recent years, due to the significant improvement in computer performance, machine learning has been applied to various fields, such as tumor and liver segmentation in CT images (Aghamohammadi et al, 2021), brain tumor segmentation (Ranjbarzadeh et al, 2021), and breast tumor segmentation in mammograms (Ranjbarzadeh et al, 2022). Also, for geophysics, underground structure segmentation, automatic velocity picking can be achieved by deep learning methods or unsupervised clustering methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering

Wang

et al. 2023

Front. Earth Sci.

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The velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth, and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on the Chan–Vese (CV) model and mean-shift clustering algorithm was proposed. It can be divided into three steps. First, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noises. Then, the velocity trend band is applied to the Chan–Vese model as the initial time condition to segment the velocity spectrum and obtain the velocity candidate region. Finally, mean-shift clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from the Chan–Vese model. When implementing the mean-shift clustering algorithm, the Gaussian kernel function and the energy of the velocity spectrum are utilized to control the efficiency and accuracy of the cluster. The tests of the model and real data prove that the proposed method can dramatically improve the accuracy and efficiency of velocity picking compared with the K-means and manual picking method.

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MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network

Cited by 40 publications

References 74 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

A Survey of Convolutional Neural Network in Breast Cancer

Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering

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