Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

Ranjbarzadeh, Ramin; Dorosti, Shadi; Ghoushchi, Saeid Jafarzadeh; Caputo, Annalina; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Arshadi, Zahra; Bendechache, Malika

doi:10.1016/j.compbiomed.2022.106443

Cited by 44 publications

(13 citation statements)

References 173 publications

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“…So, when we are facing limited computing power, this method is able to process key features with limited computing resources. In addition, this technique can also be utilized to elucidate incomprehensible neural architecture behavior [ 41 , 42 , 43 ].…”

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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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%

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

et al. 2023

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“…Simultaneously, apart from efforts aimed at augmenting the precision of neural network diagnostic outcomes, there is a discernible research emphasis on the lightweighting network models [41]. This highlights a theoretical framework wherein the primary emphasis lies in the application of DL methodologies for the classification of breast cancer images and the delineation of lesions, underscoring a pivotal role for AI in diagnostic processes [42]. Concurrently, the research thrust towards lightweighting network models reflects a strategic exploration into optimizing computational efficiency without compromising diagnostic accuracy.…”

Section: Qi Et Al Developed An Automatic Breast Cancermentioning

confidence: 99%

The application of mammography imaging in the diagnosis and prediction of breast diseases

Liu,

Wu,

Zhou

et al. 2024

PMD

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Breast diseases pose a significant threat to women’s health, so early detection and treatment are extremely important. In this context, early disease identification has become crucial in the diagnosis and treatment of breast diseases. This paper begins by outlining the pivotal role of mammography in the early diagnosis of breast cancer, comparing the structural similarities and differences between normal and diseased breast tissues. This comparison underscores the primary role of mammography in the diagnosis and treatment of breast diseases. Additionally, our paper provides an overview of fundamental concepts related to breast cancer detection, diagnosis, and prediction systems. It delves into the latest research developments in auxiliary diagnostic detection, examination, and risk prediction systems associated with breast cancer. Our objective is to offer a comprehensive understanding of the role of computer-aided detection, diagnosis, and prediction systems in breast diseases, fostering further development and application. This work aims to explore and drive innovation in the field, enhance early detection rates of breast diseases, and guide readers towards novel directions, thus contributing to female healthcare management.

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“…Max pooling selects the maximum value within a predefined region, effectively reducing the spatial dimensions of the features while retaining the most important information. This pooling operation helps to capture the most salient features and discard irrelevant or redundant information (Ranjbarzadeh, Dorosti, et al, 2022;Ranjbarzadeh, Tataei Sarshar, et al, 2022).…”

Section: Suggested Attention-based Deep Learning Modelmentioning

confidence: 99%

Attention-based deep learning model to improving multi-criteria decision-making for customer loyalty

Bayat,

Honari

2023

Am J Soc Sci Hum

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Understanding the factors that influence product loyalty is crucial for businesses to effectively attract and retain customers. This study suggests a novel approach to assess the importance and weight of criteria that lead to product loyalty by considering the Halo effect in customer decision-making. The suggested method utilizes an attention-based deep learning model to analyze customer feedback collected through the Net Promoter Score (NPS) scale, incorporating the insights of a large number of customers. The proposed method overcomes the limitations of traditional methods that rely on expert judgments or data mining, providing a more comprehensive and customer-centric perspective. By considering the Halo effect, which can lead to biased perceptions of product features, the method offers a more accurate assessment of criteria weights and their impact on product loyalty. A case study focusing on mobile phone selection and loyalty is conducted to explain the applicability and efficiency of the suggested method. The outcomes are compared with the NPS index and several common multi-criteria decision-making (MCDM) techniques. The findings highlight the superiority of the suggested method in capturing the complex relationships between criteria and product loyalty, surpassing the limitations of expert-based approaches and outperforming traditional MCDM methods. The suggested technique provides valuable insights for companies seeking to enhance customer loyalty and optimize product development strategies. However, it is important to acknowledge limitations related to the reliance on customer feedback and the contextual specificity of the results.

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Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

Cited by 44 publications

References 173 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

The application of mammography imaging in the diagnosis and prediction of breast diseases

Attention-based deep learning model to improving multi-criteria decision-making for customer loyalty

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