WARM: a new breast masses classification method by weighting association rule mining

Keyvanpour, Mohammad Reza; Shirzad, Mehrnoush Barani; Mahdikhani, Leyli

doi:10.1007/s11760-021-01989-0

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…In [38], Apriori and frequent patterns were used to identify factors associated with malignant mesothelioma. The [39] study proposed a method based on weighted ARM to identify important factor in cancer. They aim to classify the masses in mammography images into benign and malignant classes for early detection of disease.…”

Section: Related Workmentioning

confidence: 99%

Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

Dehghani,

Yazdanparast

2023

Informatics in Medicine Unlocked

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Section: Related Workmentioning

confidence: 99%

Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

Dehghani,

Yazdanparast

2023

Informatics in Medicine Unlocked

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“…Before the CAD systems were proposed, only well-trained radiologists and doctors could manually classify breast masses. However, traditional CAD systems include multiple steps such as pre-processing, segmentation, feature extraction, feature selection, and final classification [ 19 ]. Compared with deep learning-based CAD systems, traditional CAD systems are narrow and brittle [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

“…Compared to single-stage methods, multiple-staged methods aim at extracting useful features in the early stages that may benefit the classification in the later stage. In work [ 19 ], authors developed a novel breast mass classification method based on weighted association rule mining (WARM). Initially, mammograms were pre-processed for contrast enhancement while the pectoral muscle was removed.…”

Section: Related Workmentioning

confidence: 99%

DF-dRVFL: A novel deep feature based classifier for breast mass classification

Ren

Guttery

et al. 2023

Multimed Tools Appl

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Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people’s health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of $$81.71\%$$ 81.71 % . Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy.

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“…Data mining techniques allow researchers to extract information from complex data sets. These techniques help to interpret data and generate predictive models in nancial (Li et al, 2020;Nassirtoussi et al, 2014), economic (Keyvanpour et al, 2020;Yu et al, 2014), political (Jungherr, 2016), and medical (Keyvanpour et al, 2022;Mehrmolaei and Keyvanpour, 2019;Sood et al, 2021) domains and social networks (Dai and Hao, 2017;Khetarpaul, 2021;Taghvaei et al, 2021). Since users express their feelings and moods on social networks daily and impartially, data mining and machine learning techniques can be used to develop automatic diagnosis systems for mental disorders.…”

Section: Introductionmentioning

confidence: 99%

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

Keyvanpour

Mehrmolaei

Gholami

2022

Preprint

Self Cite

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Recently, depression has been raised as one of the most popular mental health disorders in the world. Also, social networks can be considered a valuable resource for mental health research due to the tendency of users for sharing their thoughts and feelings. On the other hand, text analysis of user posts relying on neural networks for such research is increasing. Neural networks have recently achieved significant success in text analysis because of the ability to automatically extract distinguishing features from data. However, neural networks are ignored the temporal and sequential nature of users' posts on social networks which affects the accuracy of the results. This shortcoming prompted us to present a more efficient method considering the sequential and temporal nature of social media users' posts. Thus, we have proposed a deep learning-based hybrid method called DDdeep to handle the mentioned challenge. There are three main features in our method, which are (1) text analysis relying on the temporal and sequential nature of posts, (2) identifying depressed users only by considering how users use language, and (3) remembering decisions because of the dependence of each post on previous posts. The DDdeep method has integrated a convolutional neural network (CNN) to extract more important features and long-short term memory (LSTM) to remember previous decisions. Our method identifies the depressed users by 78% precision, 70% recall, and 73% F1-score. Therefore, the evaluation results of our method are acceptable and competitive compared to other valid methods in this field.

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WARM: a new breast masses classification method by weighting association rule mining

Cited by 5 publications

References 26 publications

Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

DF-dRVFL: A novel deep feature based classifier for breast mass classification

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

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